{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:53:39Z","timestamp":1790272419438,"version":"4.1.0"},"reference-count":96,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T00:00:00Z","timestamp":1756512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T00:00:00Z","timestamp":1756512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cardiovasc Toxicol"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s12012-025-10055-x","type":"journal-article","created":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T18:11:39Z","timestamp":1756577499000},"page":"1732-1753","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of QTc Prolongation in Acute Poisoning with Atypical Antipsychotics Using Machine Learning Techniques: A Study from Poison Control Center"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6104-562X","authenticated-orcid":false,"given":"Asmaa Fady","family":"Sharif","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3301-0008","authenticated-orcid":false,"given":"Ahmad","family":"Hafez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9282-3167","authenticated-orcid":false,"given":"Manar Maher","family":"Fayed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7836-9639","authenticated-orcid":false,"given":"Zahraa Khalifa","family":"Sobh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,30]]},"reference":[{"key":"10055_CR1","doi-asserted-by":"publisher","first-page":"10624","DOI":"10.3390\/ijms231810624","volume":"23","author":"J Orzelska-G\u00f3rka","year":"2022","unstructured":"Orzelska-G\u00f3rka, J., Mikulska, J., Wiszniewska, A., & Bia\u0142a, G. (2022). New atypical antipsychotics in the treatment of schizophrenia and depression. International Journal of Molecular Sciences, 23, 10624.","journal-title":"International Journal of Molecular Sciences"},{"key":"10055_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03602532.2020.1717517","volume":"52","author":"M Jovanovi\u0107","year":"2020","unstructured":"Jovanovi\u0107, M., Vu\u010di\u0107evi\u0107, K., & Miljkovi\u0107, B. (2020). Understanding variability in the pharmacokinetics of atypical antipsychotics\u2014Focus on clozapine, olanzapine and aripiprazole population models. Drug Metabolism Reviews, 52, 1\u201318.","journal-title":"Drug Metabolism Reviews"},{"key":"10055_CR3","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1080\/01480545.2024.2313561","volume":"47","author":"AF Sharif","year":"2024","unstructured":"Sharif, A. F., Sobh, Z. K., Abdo, S. A. E., Alahmadi, O. M., Alharbi, H. A., Awaji, M. S., Alabdullatif, F. A., Baghlaf, A. M., Alanazi, A. F., & Fayed, M. M. (2024). Evaluation of Global Dystonia Rating Scale as a predictor of unfavorable outcomes among acute antipsychotics poisoned patients. Drug and Chemical Toxicology, 47, 386\u2013403.","journal-title":"Drug and Chemical Toxicology"},{"key":"10055_CR4","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.jpsychires.2020.05.012","volume":"127","author":"T Hata","year":"2020","unstructured":"Hata, T., Kanazawa, T., Hamada, T., Nishihara, M., Yoneda, H., Nakajima, M., & Katsumata, T. (2020). The 12-year trend report of antipsychotic usage in a nationwide claims database derived from four million people in Japan. Journal of Psychiatric Research, 127, 28\u201334.","journal-title":"Journal of Psychiatric Research"},{"key":"10055_CR5","doi-asserted-by":"publisher","first-page":"960327123118615","DOI":"10.1177\/09603271231186154","volume":"42","author":"A Sharif","year":"2023","unstructured":"Sharif, A., Aouissi, H., Kasemy, Z., Byeon, H., & Lashin, H. (2023). Development and validation of a risk prediction nomogram for disposition of acute clozapine intoxicated patients to intensive care unit. Human & Experimental Toxicology, 42, 9603271231186154.","journal-title":"Human & Experimental Toxicology"},{"key":"10055_CR6","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1093\/toxres\/tfad078","volume":"12","author":"DM El-Gharbawy","year":"2023","unstructured":"El-Gharbawy, D. M., Kabbash, I. A., & Ghonem, M. M. (2023). A nomogram proposal for early prediction of intensive care unit admission in patients with acute antipsychotic poisoning. Toxicology Research, 12, 873\u2013883.","journal-title":"Toxicology Research"},{"key":"10055_CR7","doi-asserted-by":"publisher","first-page":"83","DOI":"10.21608\/ejfsat.2020.43176.1168","volume":"22","author":"A Wahdan","year":"2020","unstructured":"Wahdan, A., El Sharkawy, S., Afandy, M., & Lashin, H. (2020). Risk factors for the need of intensive care unit admission among acute clozapine poisoned patients: A retrospective cohort study. The Egyptian Journal of Forensic Sciences and Applied Toxicology., 22, 83\u2013102.","journal-title":"The Egyptian Journal of Forensic Sciences and Applied Toxicology."},{"key":"10055_CR8","doi-asserted-by":"publisher","first-page":"51","DOI":"10.21608\/ajfm.2018.15877","volume":"31","author":"D El-Gharbawy","year":"2018","unstructured":"El-Gharbawy, D., & Ghonem, M. (2018). ECG changes as a predictive tool of outcomes in antipsychotics poisoned patients. Ain Shams Journal of Forensic Medicine and Clinical Toxicology, 31, 51\u201361.","journal-title":"Ain Shams Journal of Forensic Medicine and Clinical Toxicology"},{"key":"10055_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.21608\/ajfm.2015.18657","volume":"24","author":"S Hammad","year":"2015","unstructured":"Hammad, S., Girgis, N., Amin, S., Zanaty, A., & Abou Hatab, H. (2015). A prospective study of poisoning severity score among acute antipsychotic poisoned cases admitted to Menoufia Poison Control Center (MPCC) throughout one year. Ain Shams Journal of Forensic Medicine and Clinical Toxicology, 24, 53\u201363.","journal-title":"Ain Shams Journal of Forensic Medicine and Clinical Toxicology"},{"key":"10055_CR10","doi-asserted-by":"publisher","first-page":"tfae038","DOI":"10.1093\/toxres\/tfae038","volume":"13","author":"HK Khalifa","year":"2024","unstructured":"Khalifa, H. K., Mostafa Mansour, N., & Elmansy, A. (2024). Predictors for prolonged qt intervals in acute antipsychotic poisoned patients. Toxicology Research, 13, tfae038.","journal-title":"Toxicology Research"},{"key":"10055_CR11","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1017\/S1049023X20001533","volume":"36","author":"MM Fayed","year":"2021","unstructured":"Fayed, M. M., & Sharif, A. F. (2021). Impact of lockdown due to COVID-19 on the modalities of intoxicated patients presenting to the emergency room. Prehospital and Disaster Medicine, 36, 145\u2013162.","journal-title":"Prehospital and Disaster Medicine"},{"key":"10055_CR12","doi-asserted-by":"publisher","first-page":"601","DOI":"10.2165\/11631640-000000000-00000","volume":"26","author":"M Levine","year":"2012","unstructured":"Levine, M., & Ruha, A.-M. (2012). Overdose of atypical antipsychotics clinical presentation, mechanisms of toxicity and management. CNS Drugs, 26, 601\u2013611.","journal-title":"CNS Drugs"},{"key":"10055_CR13","volume-title":"Goldfrank's Toxicologic Emergencies, 11e","author":"D Juurlink","year":"2019","unstructured":"Juurlink, D. (2019). Antipsychotics. In L. Nelson, M. A. Howland, N. Lewin, S. Smith, L. Goldfrank, & R. Hoffman (Eds.), Goldfrank\u2019s Toxicologic Emergencies, 11e. McGraw-Hill Education."},{"key":"10055_CR14","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1093\/toxres\/tfad051","volume":"12","author":"GA Sagah","year":"2023","unstructured":"Sagah, G. A., & Fayed, M. M. (2023). Body mass index as a predictor of the outcomes of acute clozapine toxicity: A cross-sectional study. Toxicology Research, 12, 599\u2013607.","journal-title":"Toxicology Research"},{"key":"10055_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.psym.2012.11.001","volume":"54","author":"SR Beach","year":"2013","unstructured":"Beach, S. R., Celano, C. M., Noseworthy, P. A., Januzzi, J. L., & Huffman, J. C. (2013). QTc prolongation, torsades de pointes, and psychotropic medications. Psychosomatics, 54, 1\u201313.","journal-title":"Psychosomatics"},{"key":"10055_CR16","doi-asserted-by":"publisher","first-page":"101826","DOI":"10.1016\/j.toxrep.2024.101826","volume":"13","author":"HI Lashin","year":"2024","unstructured":"Lashin, H. I., Elgazzar, F. M., El sharkawy, S. I., Elsawaf, S. M., & Sobh, Z. K. (2024). Development of a risk-prediction nomogram for in-hospital adverse cardiovascular events in acute cardiotoxic agents poisoning. Toxicology Reports, 13, 101826.","journal-title":"Toxicology Reports"},{"key":"10055_CR17","doi-asserted-by":"publisher","first-page":"960327124126721","DOI":"10.1177\/09603271241267214","volume":"43","author":"HI Lashin","year":"2024","unstructured":"Lashin, H. I., Sobeeh, F. G., & Sobh, Z. K. (2024). Development and validation of a nomogram for predicting mechanical ventilation need among acutely intoxicated patients with impaired consciousness. Human & Experimental Toxicology, 43, 9603271241267214.","journal-title":"Human & Experimental Toxicology"},{"key":"10055_CR18","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1192\/bjp.bp.114.154898","volume":"208","author":"E Baer","year":"2016","unstructured":"Baer, E., Barr\u00e9, C., Fleury, C., de Montchenu, C., Garr\u00e9, J. B., Lerolle, N., & Gohier, B. (2016). Mechanical ventilation as an indicator of somatic severity of self-poisoning: Implications for psychiatric care and long-term outcomes. The British Journal of Psychiatry, 208, 280\u2013285.","journal-title":"The British Journal of Psychiatry"},{"key":"10055_CR19","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.1016\/j.mayocp.2017.05.004","volume":"92","author":"T Pham","year":"2017","unstructured":"Pham, T., Brochard, L. J., & Slutsky, A. S. (2017). Mechanical ventilation: State of the art. Mayo Clinic Proceedings, 92, 1382\u20131400.","journal-title":"Mayo Clinic Proceedings"},{"key":"10055_CR20","volume-title":"Goldfrank's Toxicologic Emergencies, 11e","author":"LS Nelson","year":"2019","unstructured":"Nelson, L. S., Howland, M. A., Lewin, N. A., Smith, S. W., Goldfrank, L. R., & Hoffman, R. S. (2019). Principles of managing the acutely poisoned or overdosed patient. In L. S. Nelson, M. A. Howland, N. A. Lewin, S. W. Smith, L. R. Goldfrank, & R. S. Hoffman (Eds.), Goldfrank\u2019s Toxicologic Emergencies, 11e. McGraw-Hill Education."},{"key":"10055_CR21","doi-asserted-by":"publisher","first-page":"204512532211049","DOI":"10.1177\/20451253221104947","volume":"12","author":"T Andric","year":"2022","unstructured":"Andric, T., Winckel, K., Tanzer, T. D., Hollingworth, S., Smith, L., Isoardi, K., Tan, O., & Siskind, D. (2022). Estimation of cardiac qtc intervals in people prescribed antipsychotics: A comparison of correction factors. Therapeutic Advances in Psychopharmacology, 12, 204512532211049.","journal-title":"Therapeutic Advances in Psychopharmacology"},{"key":"10055_CR22","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1093\/toxres\/tfac084","volume":"12","author":"A Sharif","year":"2022","unstructured":"Sharif, A., Kasemy, Z., Alshabibi, R., Almufleh, S., Abousamak, F., Alfrayan, A., Alshehri, M., Alemies, R., Almuhsen, A., & AlNasser, S. (2022). Prognostic factors in acute poisoning with central nervous system xenobiotics: Development of a nomogram predicting risk of intensive care unit admission. Toxicology Research, 12, 62\u201375.","journal-title":"Toxicology Research"},{"key":"10055_CR23","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1093\/toxres\/tfad029","volume":"12","author":"HI Lashin","year":"2023","unstructured":"Lashin, H. I., & Sharif, A. F. (2023). Evaluation of various scoring systems as predictors of the need for intensive care unit admission and other adverse outcomes among patients with acute clozapine poisoning. Toxicolology Research, 12, 468\u2013479.","journal-title":"Toxicolology Research"},{"key":"10055_CR24","doi-asserted-by":"publisher","first-page":"tfae031","DOI":"10.1093\/toxres\/tfae031","volume":"13","author":"Q Shi","year":"2024","unstructured":"Shi, Q., & Zhang, J. (2024). Clinical prediction models for intensive care unit admission in patients with acute poisoning: Is it time for a comprehensive evaluation of their utility? Toxicology Research, 13, tfae031.","journal-title":"Toxicology Research"},{"key":"10055_CR25","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.1016\/S0140-6736(17)31540-4","volume":"390","author":"T Lancet","year":"2017","unstructured":"Lancet, T. (2017). Artificial intelligence in health care: Within touching distance. Lancet (London, England), 390, 2739.","journal-title":"Lancet (London, England)"},{"key":"10055_CR26","doi-asserted-by":"crossref","unstructured":"Dineva, K., & Atanasova, T. (2020). Systematic look at machine learning algorithms\u2014advantages, disadvantages and practical applications. In International Multidisciplinary Scientific GeoConference-SGEM (pp. 317\u2013324).","DOI":"10.5593\/sgem2020\/2.1\/s07.041"},{"key":"10055_CR27","first-page":"1698","volume":"7","author":"MR Afrash","year":"2022","unstructured":"Afrash, M. R., Kazemi-Arpanahi, H., Nopour, R., Tabatabaei, E. S., & Shanbehzadeh, M. (2022). Proposing an intelligent monitoring system for early prediction of need for intubation among COVID-19 hospitalized patients. Journal of Environmental Health and Sustainable Development, 7, 1698\u20131707.","journal-title":"Journal of Environmental Health and Sustainable Development"},{"key":"10055_CR28","first-page":"1812","volume":"12","author":"A Altameem","year":"2022","unstructured":"Altameem, A., Mahanty, C., Poonia, R. C., Saudagar, A. K. J., & Kumar, R. (2022). Breast cancer detection in mammography images using deep convolutional neural networks and fuzzy ensemble modeling techniques. Diagnostics (Basel, Switzerland), 12, 1812.","journal-title":"Diagnostics (Basel, Switzerland)"},{"key":"10055_CR29","doi-asserted-by":"publisher","first-page":"153431","DOI":"10.1016\/j.tox.2023.153431","volume":"486","author":"SM Hosseini","year":"2023","unstructured":"Hosseini, S. M., Rahimi, M., Afrash, M. R., Ziaeefar, P., Yousefzadeh, P., Pashapour, S., Evini, P. E. T., Mostafazadeh, B., & Shadnia, S. (2023). Prediction of acute organophosphate poisoning severity using machine learning techniques. Toxicology, 486, 153431.","journal-title":"Toxicology"},{"key":"10055_CR30","doi-asserted-by":"publisher","first-page":"153770","DOI":"10.1016\/j.tox.2024.153770","volume":"504","author":"M Rahimi","year":"2024","unstructured":"Rahimi, M., Hosseini, S. M., Mohtarami, S. A., Mostafazadeh, B., Evini, P. E. T., Fathy, M., Kazemi, A., Khani, S., Mortazavi, S. M., Soheili, A., Vahabi, S. M., & Shadnia, S. (2024). Prediction of acute methanol poisoning prognosis using machine learning techniques. Toxicology, 504, 153770.","journal-title":"Toxicology"},{"key":"10055_CR31","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1177\/0960327121991910","volume":"40","author":"B Behnoush","year":"2021","unstructured":"Behnoush, B., Bazmi, E., Nazari, S. H., Khodakarim, S., Looha, M. A., & Soori, H. (2021). Machine learning algorithms to predict seizure due to acute tramadol poisoning. Human & Experimental Toxicology, 40, 1225\u20131233.","journal-title":"Human & Experimental Toxicology"},{"key":"10055_CR32","doi-asserted-by":"publisher","first-page":"897","DOI":"10.3390\/ijerph17030897","volume":"17","author":"Y Choi","year":"2020","unstructured":"Choi, Y., & Boo, Y. (2020). Comparing logistic regression models with alternative machine learning methods to predict the risk of drug intoxication mortality. International Journal of Environmental Research and Public Health, 17, 897.","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"10055_CR33","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1111\/sltb.12591","volume":"50","author":"D Liu","year":"2020","unstructured":"Liu, D., Yu, M., Duncan, J., Fondario, A., Kharrazi, H., & Nestadt, P. S. (2020). Discovering the unclassified suicide cases among undetermined drug overdose deaths using machine learning techniques. Suicide & Life-Threatening Behavior, 50, 333\u2013344.","journal-title":"Suicide & Life-Threatening Behavior"},{"key":"10055_CR34","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s12911-024-02443-0","volume":"24","author":"M Rahimi","year":"2024","unstructured":"Rahimi, M., Afrash, M. R., Shadnia, S., Mostafazadeh, B., Evini, P. E. T., Bardsiri, M. S., & Ramezani, M. (2024). Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms. BMC Medical Informatics and Decision Making, 24, 38.","journal-title":"BMC Medical Informatics and Decision Making"},{"key":"10055_CR35","first-page":"e078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., Boulesteix, A. L., Camaradou, J. C., Celi, L. A., Denaxas, S., Denniston, A. K., Glocker, B., Golub, R. M., Harvey, H., Heinze, G., \u2026 Logullo, P. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ (Clinical Research Ed.), 385, e078378.","journal-title":"BMJ (Clinical Research Ed.)"},{"key":"10055_CR36","doi-asserted-by":"publisher","first-page":"2455","DOI":"10.1177\/0962280218784726","volume":"28","author":"M Van Smeden","year":"2019","unstructured":"Van Smeden, M., Moons, K. G., de Groot, J. A., Collins, G. S., Altman, D. G., Eijkemans, M. J., & Reitsma, J. B. (2019). Sample size for binary logistic prediction models: Beyond events per variable criteria. Statistical Methods in Medical Research, 28, 2455\u20132474.","journal-title":"Statistical Methods in Medical Research"},{"key":"10055_CR37","doi-asserted-by":"publisher","first-page":"500","DOI":"10.7334\/psicothema2013.23","volume":"25","author":"JJ Monta\u00f1o Moreno","year":"2013","unstructured":"Monta\u00f1o Moreno, J. J., Palmer Pol, A., Ses\u00e9 Abad, A., & Cajal Blasco, B. (2013). Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25, 500\u2013506.","journal-title":"Psicothema"},{"key":"10055_CR38","unstructured":"International Classification of Diseases, 10th revision (ICD-10). Poisoning by, adverse effect of and underdosing of other antipsychotics and neuroleptics. (2025). Retrieved January 2025, from https:\/\/www.icd10data.com\/ICD10CM\/Codes\/S00-T88\/T36-T50\/T43-"},{"key":"10055_CR39","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1186\/s12888-022-03755-z","volume":"22","author":"N Pai","year":"2022","unstructured":"Pai, N., Acar, M., Juneja, P., Kouhkamari, M. H., Siva, S., & Mullan, J. (2022). Antipsychotic prescribing patterns in Australia: A retrospective analysis. BMC Psychiatry, 22, 110.","journal-title":"BMC Psychiatry"},{"key":"10055_CR40","unstructured":"World Health Organization (WHO). The use of the WHO-UMC system for standardised case causality assessment. (2013). Retrieved June 1, 2025, from https:\/\/www.who.int\/publications\/m\/item\/WHO-causality-assessment"},{"key":"10055_CR41","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.3390\/s23042333","volume":"23","author":"S Szeghalmy","year":"2023","unstructured":"Szeghalmy, S., & Fazekas, A. (2023). A comparative study of the use of stratified cross-validation and distribution-balanced stratified cross-validation in imbalanced learning. Sensors (Basel), 23, 2333.","journal-title":"Sensors (Basel)"},{"key":"10055_CR42","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.3390\/electronics10161973","volume":"10","author":"DS Soper","year":"2021","unstructured":"Soper, D. S. (2021). Greed is good: Rapid hyperparameter optimization and model selection using greedy k-fold cross validation. Electronics, 10, 1973.","journal-title":"Electronics"},{"key":"10055_CR43","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1111\/j.1542-474X.1997.tb00325.x","volume":"2","author":"HC Bazett","year":"1997","unstructured":"Bazett, H. C. (1997). An analysis of the time-relations of electrocardiograms. Annals of Noninvasive Electrocardiology, 2, 177\u2013194.","journal-title":"Annals of Noninvasive Electrocardiology"},{"key":"10055_CR44","doi-asserted-by":"publisher","first-page":"e12804","DOI":"10.1111\/anec.12804","volume":"26","author":"P Dahlberg","year":"2021","unstructured":"Dahlberg, P., Diamant, U. B., Gilljam, T., Rydberg, A., & Bergfeldt, L. (2021). QT correction using Bazett\u2019s formula remains preferable in long QT syndrome type 1 and 2. Annals of Noninvasive Electrocardiology, 26, e12804.","journal-title":"Annals of Noninvasive Electrocardiology"},{"key":"10055_CR45","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.jelectrocard.2019.06.009","volume":"56","author":"A Dash","year":"2019","unstructured":"Dash, A., Torado, C., Paw, N., Fan, D., Pezeshkian, N., & Srivatsa, U. (2019). QT correction in atrial fibrillation\u2014measurement revisited. Journal of Electrocardiology, 56, 70\u201376.","journal-title":"Journal of Electrocardiology"},{"key":"10055_CR46","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1016\/j.amjcard.2019.02.057","volume":"123","author":"J Tooley","year":"2019","unstructured":"Tooley, J., Ouyang, D., Hadley, D., Turakhia, M., Wang, P., Ashley, E., Froelicher, V., & Perez, M. (2019). Comparison of QT interval measurement methods and correction formulas in atrial fibrillation. The American Journal of Cardiology, 123, 1822\u20131827.","journal-title":"The American Journal of Cardiology"},{"key":"10055_CR47","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.2165\/00003495-200262110-00006","volume":"62","author":"P Haddad","year":"2002","unstructured":"Haddad, P., & Anderson, I. (2002). Antipsychotic-related QTc prolongation, torsade de pointes and sudden death. Drugs, 62, 1649\u20131671.","journal-title":"Drugs"},{"key":"10055_CR48","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.jpainsymman.2019.12.356","volume":"59","author":"JR Hardy","year":"2020","unstructured":"Hardy, J. R., Bundock, D., Cross, J., Gibbons, K., Pinkerton, R., Kindl, K., Good, P., & Philip, J. (2020). Prevalence of QTc prolongation in patients with advanced cancer receiving palliative care-a cause for concern? Journal of Pain and Symptom Management, 59, 856\u2013863.","journal-title":"Journal of Pain and Symptom Management"},{"key":"10055_CR49","doi-asserted-by":"publisher","first-page":"1250909","DOI":"10.3389\/fmicb.2023.1250909","volume":"14","author":"E Ibrahimi","year":"2023","unstructured":"Ibrahimi, E., Lopes, M. B., Dhamo, X., Simeon, A., Shigdel, R., Hron, K., Stres, B., D\u2019Elia, D., Berland, M., & Marcos-Zambrano, L. J. (2023). Overview of data preprocessing for machine learning applications in human microbiome research. Frontiers in Microbiology, 14, 1250909.","journal-title":"Frontiers in Microbiology"},{"key":"10055_CR50","doi-asserted-by":"publisher","first-page":"S21","DOI":"10.1016\/j.chest.2020.03.011","volume":"158","author":"M Etminan","year":"2020","unstructured":"Etminan, M., Collins, G. S., & Mansournia, M. A. (2020). Using causal diagrams to improve the design and interpretation of medical research. Chest, 158, S21-s28.","journal-title":"Chest"},{"key":"10055_CR51","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-031-22206-1_2","volume-title":"A guide to applied machine learning for biologists","author":"HB Mehare","year":"2023","unstructured":"Mehare, H. B., Anilkumar, J. P., & Usmani, N. A. (2023). The Python programming language. In M. S. Badar (Ed.), A guide to applied machine learning for biologists (pp. 27\u201360). Springer."},{"key":"10055_CR52","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/S1532-0464(03)00034-0","volume":"35","author":"S Dreiseitl","year":"2002","unstructured":"Dreiseitl, S., & Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: A methodology review. Journal of Biomedical Informatics, 35, 352\u2013359.","journal-title":"Journal of Biomedical Informatics"},{"key":"10055_CR53","doi-asserted-by":"publisher","first-page":"2967","DOI":"10.1007\/s00500-015-1925-9","volume":"20","author":"P Mistry","year":"2016","unstructured":"Mistry, P., Neagu, D., Trundle, P. R., & Vessey, J. D. (2016). Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology. Soft Computing, 20, 2967\u20132979.","journal-title":"Soft Computing"},{"key":"10055_CR54","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","volume":"7","author":"JT Hancock","year":"2020","unstructured":"Hancock, J. T., & Khoshgoftaar, T. M. (2020). CatBoost for big data: An interdisciplinary review. Journal of Big Data, 7, 94.","journal-title":"Journal of Big Data"},{"key":"10055_CR55","doi-asserted-by":"publisher","first-page":"25","DOI":"10.24138\/jcomss.v11i1.114","volume":"11","author":"P Janardhanan","year":"2015","unstructured":"Janardhanan, P., Heena, L., & Sabika, F. (2015). Effectiveness of support vector machines in medical data mining. Journal of Communications Software and Systems, 11, 25\u201330.","journal-title":"Journal of Communications Software and Systems"},{"key":"10055_CR56","doi-asserted-by":"publisher","first-page":"100200","DOI":"10.1016\/j.imu.2019.100200","volume":"16","author":"G Battineni","year":"2019","unstructured":"Battineni, G., Chintalapudi, N., & Amenta, F. (2019). Machine learning in medicine: Performance calculation of dementia prediction by Support Vector Machines (SVM). Informatics in Medicine Unlocked, 16, 100200.","journal-title":"Informatics in Medicine Unlocked"},{"key":"10055_CR57","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-981-15-0633-8_22","volume-title":"International conference on intelligent computing and smart communication 2019","author":"P Bahad","year":"2020","unstructured":"Bahad, P., & Saxena, P. (2020). Study of AdaBoost and gradient boosting algorithms for predictive analytics. In G. Singh Tomar, N. S. Chaudhari, J. L. V. Barbosa, & M. K. Aghwariya (Eds.), International conference on intelligent computing and smart communication 2019 (pp. 235\u2013244). Springer."},{"key":"10055_CR58","doi-asserted-by":"publisher","first-page":"20132","DOI":"10.1038\/s41598-021-99505-4","volume":"11","author":"P Wongyikul","year":"2021","unstructured":"Wongyikul, P., Thongyot, N., Tantrakoolcharoen, P., Seephueng, P., & Khumrin, P. (2021). High alert drugs screening using gradient boosting classifier. Scientific Reports, 11, 20132.","journal-title":"Scientific Reports"},{"key":"10055_CR59","doi-asserted-by":"publisher","DOI":"10.1136\/jim-2021-002278","author":"A Ram\u00f3n","year":"2022","unstructured":"Ram\u00f3n, A., Torres, A. M., Milara, J., Casc\u00f3n, J., Blasco, P., & Mateo, J. (2022). eXtreme gradient boosting-based method to classify patients with COVID-19. Journal of Investigative Medicine. https:\/\/doi.org\/10.1136\/jim-2021-002278","journal-title":"Journal of Investigative Medicine"},{"key":"10055_CR60","doi-asserted-by":"publisher","first-page":"e5927","DOI":"10.1002\/cpe.5927","volume":"32","author":"R Swami","year":"2020","unstructured":"Swami, R., Dave, M., & Ranga, V. (2020). Voting-based intrusion detection framework for securing software-defined networks. Concurrency and Computation: Practice & Experience, 32, e5927.","journal-title":"Concurrency and Computation: Practice & Experience"},{"key":"10055_CR61","doi-asserted-by":"publisher","first-page":"220990","DOI":"10.1109\/ACCESS.2020.3042848","volume":"8","author":"D Zhang","year":"2020","unstructured":"Zhang, D., & Gong, Y. (2020). The comparison of LightGBM and XGBoost coupling factor analysis and prediagnosis of acute liver failure. IEEE Access, 8, 220990\u2013221003.","journal-title":"IEEE Access"},{"key":"10055_CR62","doi-asserted-by":"publisher","first-page":"6256","DOI":"10.1038\/s41598-022-10358-x","volume":"12","author":"S Uddin","year":"2022","unstructured":"Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12, 6256.","journal-title":"Scientific Reports"},{"key":"10055_CR63","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1111\/bcpt.13800","volume":"131","author":"O Mehrpour","year":"2022","unstructured":"Mehrpour, O., Hoyte, C., Delva-Clark, H., Al Masud, A., Biswas, A., Schimmel, J., Nakhaee, S., & Goss, F. (2022). Classification of acute poisoning exposures with machine learning models derived from the National Poison Data System. Basic & Clinical Pharmacology & Toxicology, 131, 566\u2013574.","journal-title":"Basic & Clinical Pharmacology & Toxicology"},{"key":"10055_CR64","doi-asserted-by":"publisher","first-page":"195","DOI":"10.4103\/tjem.tjem_182_23","volume":"23","author":"\u015eK \u00c7orbac\u0131o\u011flu","year":"2023","unstructured":"\u00c7orbac\u0131o\u011flu, \u015eK., & Aksel, G. (2023). Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value. Turkish Journal of Emergency Medicine., 23, 195\u2013198.","journal-title":"Turkish Journal of Emergency Medicine."},{"key":"10055_CR65","first-page":"209","volume-title":"Encyclopedia of machine learning","author":"KM Ting","year":"2010","unstructured":"Ting, K. M. (2010). Confusion matrix. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 209\u2013209). Springer."},{"key":"10055_CR66","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1016\/j.jemermed.2012.03.002","volume":"43","author":"AB Minns","year":"2012","unstructured":"Minns, A. B., & Clark, R. F. (2012). Toxicology and overdose of atypical antipsychotics. The Journal of Emergency Medicine, 43, 906\u2013913.","journal-title":"The Journal of Emergency Medicine"},{"key":"10055_CR67","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.1016\/S0140-6736(00)02035-3","volume":"355","author":"JG Reilly","year":"2000","unstructured":"Reilly, J. G., Ayis, S. A., Ferrier, I. N., Jones, S. J., & Thomas, S. H. L. (2000). QTc-interval abnormalities and psychotropic drug therapy in psychiatric patients. Lancet, 355, 1048\u20131052.","journal-title":"Lancet"},{"key":"10055_CR68","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1111\/j.1472-8206.1994.tb00818.x","volume":"8","author":"MM Adamantidis","year":"1994","unstructured":"Adamantidis, M. M., Kerram, P., & Dupuis, B. A. (1994). In vitro electrophysiological detection of iatrogenic arrhythmogenicity. Fundamental & Clinical Pharmacology, 8, 391\u2013407.","journal-title":"Fundamental & Clinical Pharmacology"},{"key":"10055_CR69","doi-asserted-by":"publisher","first-page":"35","DOI":"10.2165\/00002018-200528010-00003","volume":"28","author":"K Titier","year":"2005","unstructured":"Titier, K., Girodet, P. O., Verdoux, H., Molimard, M., B\u00e9gaud, B., Haverkamp, W., Lader, M., & Moore, N. (2005). Atypical antipsychotics: From potassium channels to torsade de pointes and sudden death. Drug Safety, 28, 35\u201351.","journal-title":"Drug Safety"},{"key":"10055_CR70","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/S0196-0644(99)70243-X","volume":"34","author":"LG Cohen","year":"1999","unstructured":"Cohen, L. G., Fatalo, A., Thompson, B. T., Di Centes Bergeron, G., Flood, J. G., & Poupolo, P. R. (1999). Olanzapine overdose with serum concentrations. Annals of Emergency Medicine, 34, 275\u2013278.","journal-title":"Annals of Emergency Medicine"},{"key":"10055_CR71","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.1176\/appi.ps.201300514","volume":"65","author":"K Viverito","year":"2014","unstructured":"Viverito, K., Owen, R., Mittal, D., Li, C., & Williams, J. (2014). Management of new hyperglycemia in patients prescribed antipsychotics. Psychiatric Services, 65, 1502\u20131505.","journal-title":"Psychiatric Services"},{"key":"10055_CR72","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s40780-015-0015-6","volume":"1","author":"Y Kato","year":"2015","unstructured":"Kato, Y., Umetsu, R., Abe, J., Ueda, N., Nakayama, Y., Kinosada, Y., & Nakamura, M. (2015). Hyperglycemic adverse events following antipsychotic drug administration in spontaneous adverse event reports. Journal of Pharmaceutical Health Care and Sciences, 1, 15.","journal-title":"Journal of Pharmaceutical Health Care and Sciences"},{"key":"10055_CR73","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s40265-013-0171-7","volume":"74","author":"TV Liao","year":"2014","unstructured":"Liao, T. V., & Phan, S. V. (2014). Acute hyperglycemia associated with short-term use of atypical antipsychotic medications. Drugs, 74, 183\u2013194.","journal-title":"Drugs"},{"issue":"Suppl 23","key":"10055_CR74","first-page":"30","volume":"62","author":"JP Lindenmayer","year":"2001","unstructured":"Lindenmayer, J. P., Nathan, A. M., & Smith, R. C. (2001). Hyperglycemia associated with the use of atypical antipsychotics. The Journal of Clinical Psychiatry, 62(Suppl 23), 30\u201338.","journal-title":"The Journal of Clinical Psychiatry"},{"key":"10055_CR75","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1016\/j.euroneuro.2018.06.008","volume":"28","author":"M Marcussen","year":"2018","unstructured":"Marcussen, M., Polcwiartek, C., Mills, E. H. A., Jirback, F., Bylin, P., Torp-Pedersen, C., S\u00f8gaard, P., & Kragholm, K. (2018). Abnormal serum potassium levels and 6-month all-cause mortality in patients co-treated with antipsychotic and diuretic drugs\u2014a Danish register-based cohort study. European Neuropsychopharmacology, 28, 1006\u20131014.","journal-title":"European Neuropsychopharmacology"},{"key":"10055_CR76","doi-asserted-by":"publisher","first-page":"617428","DOI":"10.3389\/fpsyt.2020.617428","volume":"11","author":"HN Boyda","year":"2020","unstructured":"Boyda, H. N., Ho, A. A., Tse, L., Procyshyn, R. M., Yuen, J. W. Y., Kim, D. D., Honer, W. G., & Barr, A. M. (2020). Differential effects of acute treatment with antipsychotic drugs on peripheral catecholamines. Frontiers in Psychiatry, 11, 617428.","journal-title":"Frontiers in Psychiatry"},{"key":"10055_CR77","doi-asserted-by":"publisher","first-page":"365","DOI":"10.4103\/ijn.IJN_225_17","volume":"28","author":"G Chandramohan","year":"2018","unstructured":"Chandramohan, G., Dineshkumar, T., Arul, R., Seenivasan, M., Dhanapriya, J., Sakthirajan, R., Balasubramaniyan, T., & Gopalakrishnan, N. (2018). Spectrum of hypokalemic paralysis from a tertiary care center in India. Indian Journal of Nephrology, 28, 365\u2013369.","journal-title":"Indian Journal of Nephrology"},{"key":"10055_CR78","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1093\/toxres\/tfad023","volume":"12","author":"A Sharif","year":"2023","unstructured":"Sharif, A., Kasemy, Z., Mabrouk, H., Shoeib, O., & Fayed, M. (2023). Could the serum glucose\/potassium ratio offer an early reliable predictor of life-threatening events in acute methylxanthine intoxication? Toxicology Research, 12, 310\u2013320.","journal-title":"Toxicology Research"},{"key":"10055_CR79","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neuro.2022.02.001","volume":"89","author":"A Sharif","year":"2022","unstructured":"Sharif, A., & Fayed, M. (2022). Assessment of the serum glucose\/potassium GLU\/K ratio as a predictor of intermediate syndrome following acute anticholinesterase exposure. Neurotoxicology, 89, 161\u2013173.","journal-title":"Neurotoxicology"},{"key":"10055_CR80","doi-asserted-by":"publisher","first-page":"tfae168","DOI":"10.1093\/toxres\/tfae168","volume":"13","author":"AM Elmansy","year":"2024","unstructured":"Elmansy, A. M., Hannora, D. M., & Khalifa, H. K. (2024). Serum glucose\/potassium ratio as an indicator of early and delayed outcomes of acute carbon monoxide poisoning. Toxicology Research, 13, tfae168.","journal-title":"Toxicology Research"},{"key":"10055_CR81","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1186\/s12873-025-01223-x","volume":"25","author":"GG Ararame","year":"2025","unstructured":"Ararame, G. G., Senbeta, B. S., & Chereka, A. A. (2025). Prevalence and determinant of poor treatment outcome of poisoning in Ethiopia: Systematic review and meta-analysis. Bmc Emergency Medicine, 25, 62.","journal-title":"Bmc Emergency Medicine"},{"key":"10055_CR82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejfs.2012.12.001","volume":"3","author":"MK El Masry","year":"2013","unstructured":"El Masry, M. K., & Azab, S. M. S. (2013). Inappropriate management and transfer of cases with acute poisoning referred to poisoning treatment center\u2014Ain Shams University\u2014Cairo. Egyptian Journal of Forensic Sciences, 3, 1\u20137.","journal-title":"Egyptian Journal of Forensic Sciences"},{"key":"10055_CR83","doi-asserted-by":"publisher","first-page":"14468","DOI":"10.1038\/s41598-025-94395-2","volume":"15","author":"O Mehrpour","year":"2025","unstructured":"Mehrpour, O., Vohra, V., Nakhaee, S., Mohtarami, S. A., & Shirazi, F. M. (2025). Machine learning for predicting medical outcomes associated with acute lithium poisoning. Scientific Reports, 15, 14468.","journal-title":"Scientific Reports"},{"key":"10055_CR84","doi-asserted-by":"publisher","first-page":"e0291908","DOI":"10.1371\/journal.pone.0291908","volume":"18","author":"GM Foody","year":"2023","unstructured":"Foody, G. M. (2023). Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient. PLoS ONE, 18, e0291908.","journal-title":"PLoS ONE"},{"key":"10055_CR85","first-page":"389","volume":"2019","author":"X Dong","year":"2019","unstructured":"Dong, X., Rashidian, S., Wang, Y., Hajagos, J., Zhao, X., Rosenthal, R. N., Kong, J., Saltz, M., Saltz, J., & Wang, F. (2019). Machine learning based opioid overdose prediction using electronic health records. AMIA Annual Symposium Proceedings., 2019, 389\u2013398.","journal-title":"AMIA Annual Symposium Proceedings."},{"key":"10055_CR86","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1111\/bcpt.13865","volume":"133","author":"O Mehrpour","year":"2023","unstructured":"Mehrpour, O., Saeedi, F., Vohra, V., Abdollahi, J., Shirazi, F. M., & Goss, F. (2023). The role of decision tree and machine learning models for outcome prediction of bupropion exposure: A nationwide analysis of more than 14 000 patients in the United States. Basic & Clinical Pharmacology & Toxicology, 133, 98\u2013110.","journal-title":"Basic & Clinical Pharmacology & Toxicology"},{"key":"10055_CR87","doi-asserted-by":"publisher","first-page":"16101","DOI":"10.1038\/s41598-024-67257-6","volume":"14","author":"H Li","year":"2024","unstructured":"Li, H., Liu, Z., Sun, W., Li, T., & Dong, X. (2024). Interpretable machine learning for the prediction of death risk in patients with acute diquat poisoning. Scientific Reports, 14, 16101.","journal-title":"Scientific Reports"},{"key":"10055_CR88","doi-asserted-by":"publisher","unstructured":"Tang, G., Jiang, Z., Wei, W., Yang, Y., Yang, S., & Yao, R. (2023). Comparison of machine learning models in predicting in-hospital mortality of patients with acute paraquat poisoning. Research Square. https:\/\/doi.org\/10.21203\/rs.3.rs-3017555\/v1.","DOI":"10.21203\/rs.3.rs-3017555\/v1"},{"key":"10055_CR89","doi-asserted-by":"publisher","first-page":"1","DOI":"10.12785\/ijcds\/1571110606","volume":"17","author":"R Talal","year":"2025","unstructured":"Talal, R., & Aldabagh, H. (2025). Prediction of drug risks consumption by using artificial intelligence techniques. International Journal of Computing and Digital Systems, 17, 1\u201311.","journal-title":"International Journal of Computing and Digital Systems"},{"key":"10055_CR90","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1080\/01480545.2023.2277128","volume":"47","author":"O Mehrpour","year":"2024","unstructured":"Mehrpour, O., Farhad, S., Varun, V., & Hoyte, C. (2024). Outcome prediction of methadone poisoning in the United States: Implications of machine learning in the National Poison Data System (NPDS). Drug and Chemical Toxicology, 47, 556\u2013563.","journal-title":"Drug and Chemical Toxicology"},{"key":"10055_CR91","doi-asserted-by":"publisher","first-page":"e17337","DOI":"10.1016\/j.heliyon.2023.e17337","volume":"9","author":"Y Veisani","year":"2023","unstructured":"Veisani, Y., Sayyadi, H., Sahebi, A., Moradi, G., Mohamadian, F., & Delpisheh, A. (2023). Comparison of machine learning algorithms to predict intentional and unintentional poisoning risk factors. Heliyon, 9, e17337.","journal-title":"Heliyon"},{"key":"10055_CR92","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s10994-005-0466-3","volume":"59","author":"N Landwehr","year":"2005","unstructured":"Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine Learning, 59, 161\u2013205.","journal-title":"Machine Learning"},{"key":"10055_CR93","doi-asserted-by":"publisher","first-page":"S574","DOI":"10.21037\/jtd.2019.01.25","volume":"11","author":"ME Shipe","year":"2019","unstructured":"Shipe, M. E., Deppen, S. A., Farjah, F., & Grogan, E. L. (2019). Developing prediction models for clinical use using logistic regression: An overview. Journal of Thoracic Disease, 11, S574-s584.","journal-title":"Journal of Thoracic Disease"},{"key":"10055_CR94","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1213\/ANE.0000000000005247","volume":"132","author":"P Schober","year":"2021","unstructured":"Schober, P., & Vetter, T. R. (2021). Logistic regression in medical research. Anesthesia and Analgesia, 132, 365\u2013366.","journal-title":"Anesthesia and Analgesia"},{"key":"10055_CR95","doi-asserted-by":"publisher","first-page":"455","DOI":"10.3390\/medicina56090455","volume":"56","author":"HSR Rajula","year":"2020","unstructured":"Rajula, H. S. R., Verlato, G., Manchia, M., Antonucci, N., & Fanos, V. (2020). Comparison of conventional statistical methods with machine learning in medicine: Diagnosis, drug development, and treatment. Medicina, 56, 455.","journal-title":"Medicina"},{"key":"10055_CR96","doi-asserted-by":"publisher","first-page":"113","DOI":"10.21608\/ajfm.2019.43103","volume":"33","author":"M Mubarak","year":"2019","unstructured":"Mubarak, M., El Madah, E., El Gharbawy, D., & Ashmawy, M. (2019). Assessment of acute antipsychotic poisoned cases admitted to Tanta University Poison Control Unit. Ain Shams Journal of Forensic Medicine and Clinical Toxicology, 33, 113\u2013125.","journal-title":"Ain Shams Journal of Forensic Medicine and Clinical Toxicology"}],"container-title":["Cardiovascular Toxicology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12012-025-10055-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12012-025-10055-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12012-025-10055-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:02:42Z","timestamp":1760144562000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12012-025-10055-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,30]]},"references-count":96,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10055"],"URL":"https:\/\/doi.org\/10.1007\/s12012-025-10055-x","relation":{},"ISSN":["1530-7905","1559-0259"],"issn-type":[{"value":"1530-7905","type":"print"},{"value":"1559-0259","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,30]]},"assertion":[{"value":"7 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The current study was conducted after receiving ethical approval from the Institutional Review Board (IRB) at Tanta University, Faculty of Medicine (Approval Number FWA00022834\/1RB0010038\/36264PR765724).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}