{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T16:17:14Z","timestamp":1791389834632,"version":"4.3.3"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Shanghai Nursing Association Research Project","award":["2024MS-B21"],"award-info":[{"award-number":["2024MS-B21"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72074168"],"award-info":[{"award-number":["72074168"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["ICMx"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Mechanical ventilation is a critical life support technology in the intensive care unit. However, the weaning process remains complex, making the optimal timing for liberation from ventilation challenging to ascertain and imposing a considerable clinical workload. Additionally, advanced weaning assistance tools that integrate multidimensional clinical factors to help clinical staff make precise decisions during the weaning process are lacking. The aim of this study to develop and validate an interpretable machine learning model that comprehensively evaluates the factors influencing weaning to provide clinical decision support for weaning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>We collected data from the ICU of Shanghai Tenth People\u2019s Hospital and its affiliated hospitals. Ten distinct machine learning algorithms for predicting extubation outcomes in patients receiving mechanical ventilation were developed and internally validated. Model performance was quantified using the area under the receiver operating characteristic curve AUC, overall accuracy, sensitivity, specificity, and F1 score. NRI, IDI, and DCA were used to comprehensively identify the optimal model. The relative contribution of each predictor was ranked and compared through SHAP analysis, and the best-performing model was externally validated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Through univariate and LASSO analyses, 24 predictive variables for machine learning model construction were identified. Comprehensive evaluation showed that among the candidate algorithms, the LGB model demonstrated the highest overall performance. SHAP analysis revealed that the top-ranked features for predicting successful liberation from mechanical ventilation were creatinine levels, lactate levels, the level of consciousness, SpO2, systolic blood pressure, cough reflex, chronic respiratory disease, diastolic blood pressure, and age.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The optimized predictive model, which was developed through the integration of multidimensional predictive factors with diverse machine learning algorithms, exhibits superior predictive accuracy and demonstrates significant clinical potential for determining the optimal timing for weaning patients receiving invasive mechanical ventilation.<\/jats:p>\n                    <jats:p>\n                      <jats:italic>Trial registration<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>Current Controlled Trials ChiCTR2400093658; registration date: December 10, 2024.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40635-026-00859-8","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T06:04:44Z","timestamp":1768975484000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU"],"prefix":"10.1186","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2401-3474","authenticated-orcid":false,"given":"Changcui","family":"Qiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lulu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yugang","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunwei","family":"Chi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangwei","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"issue":"12","key":"859_CR1","first-page":"197","volume":"117","author":"WDDGJ Windisch","year":"2020","unstructured":"Windisch WDDGJ (2020) Prolonged weaning from mechanical ventilation. Dtsch Arztebl Int 117(12):197\u2013204","journal-title":"Dtsch Arztebl Int"},{"key":"859_CR2","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/s40560-019-0402-4","volume":"7","author":"K Nitta","year":"2019","unstructured":"Nitta K, Okamoto K, Imamura H et al (2019) A comprehensive protocol for ventilator weaning and extubation: a prospective observational study. J Intensive Care 7:50","journal-title":"J Intensive Care"},{"issue":"5","key":"859_CR3","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/S2213-2600(22)00449-0","volume":"11","author":"T Pham","year":"2023","unstructured":"Pham T, Heunks L, Bellani G et al (2023) Weaning from mechanical ventilation in intensive care units across 50 countries (WEAN SAFE): a multicentre, prospective, observational cohort study. Lancet Respir Med 11(5):465\u2013476","journal-title":"Lancet Respir Med"},{"issue":"1","key":"859_CR4","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s40635-017-0121-x","volume":"5","author":"GF Nieman","year":"2017","unstructured":"Nieman GF, Satalin J, Andrews P et al (2017) Personalizing mechanical ventilation according to physiologic parameters to stabilize alveoli and minimize ventilator induced lung injury (VILI). Intensive Care Med Exp 5(1):8","journal-title":"Intensive Care Med Exp"},{"issue":"12","key":"859_CR5","doi-asserted-by":"publisher","first-page":"1751","DOI":"10.1007\/s00134-022-06919-3","volume":"48","author":"G Hernandez","year":"2022","unstructured":"Hernandez G, Paredes I, Moran F et al (2022) Effect of postextubation noninvasive ventilation with active humidification vs high-flow nasal cannula on reintubation in patients at very high risk for extubation failure: a randomized trial. Intensive Care Med 48(12):1751\u20131759","journal-title":"Intensive Care Med"},{"issue":"9","key":"859_CR6","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1159\/000533333","volume":"102","author":"JD Michels","year":"2023","unstructured":"Michels JD, Trudzinski FC, Bornitz F et al (2023) Costs of weaning failure: a prospective, multicentre, controlled, non-randomised, interventional study on economic implications for the German health care system. Respiration 102(9):813\u2013820","journal-title":"Respiration"},{"key":"859_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13051505","author":"T Stivi","year":"2024","unstructured":"Stivi T, Padawer D, Dirini N et al (2024) Using artificial intelligence to predict mechanical ventilation weaning success in patients with respiratory failure, including those with acute respiratory distress syndrome. J Clin Med. https:\/\/doi.org\/10.3390\/jcm13051505","journal-title":"J Clin Med"},{"issue":"3","key":"859_CR8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000478","volume":"3","author":"S Sheikhalishahi","year":"2024","unstructured":"Sheikhalishahi S, Kaspar M, Zaghdoudi S et al (2024) Predicting successful weaning from mechanical ventilation by reduction in positive end-expiratory pressure level using machine learning. PLOS Digital Health 3(3):e478","journal-title":"PLOS Digital Health"},{"key":"859_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102087","volume":"117","author":"Y Jia","year":"2021","unstructured":"Jia Y, Kaul C, Lawton T et al (2021) Prediction of weaning from mechanical ventilation using convolutional neural networks. Artif Intell Med 117:102087","journal-title":"Artif Intell Med"},{"key":"859_CR10","doi-asserted-by":"publisher","first-page":"150960","DOI":"10.1109\/ACCESS.2019.2946980","volume":"7","author":"T Chen","year":"2019","unstructured":"Chen T, Xu J, Ying H et al (2019) Prediction of extubation failure for intensive care unit patients using light gradient boosting machine. IEEE Access 7:150960\u2013150968","journal-title":"IEEE Access"},{"issue":"1","key":"859_CR11","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1186\/s13054-025-05341-7","volume":"29","author":"O Khazaei","year":"2025","unstructured":"Khazaei O, Laffey CM, Sheerin R et al (2025) Impact of comorbidities on management and outcomes of patients weaning from invasive mechanical ventilation: insights from the WEAN SAFE study. Crit Care 29(1):114","journal-title":"Crit Care"},{"key":"859_CR12","doi-asserted-by":"crossref","unstructured":"Collins GS M K D P. (2024) TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. q902.","DOI":"10.1136\/bmj.q902"},{"key":"859_CR13","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m441","volume":"368","author":"RD Riley","year":"2020","unstructured":"Riley RD, Ensor J, Snell KIE et al (2020) Calculating the sample size required for developing a clinical prediction model. BMJ 368:m441","journal-title":"BMJ"},{"issue":"1","key":"859_CR14","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1164\/rccm.201610-2075ST","volume":"195","author":"TD Girard","year":"2017","unstructured":"Girard TD, Alhazzani W, Kress JP et al (2017) An official American Thoracic Society\/American College of Chest Physicians clinical practice guideline: liberation from mechanical ventilation in critically ill adults. Rehabilitation protocols, ventilator liberation protocols, and cuff leak tests. Am J Respir Crit Care Med 195(1):120\u2013133","journal-title":"Am J Respir Crit Care Med"},{"issue":"4","key":"859_CR15","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1111\/biom.13372","volume":"77","author":"LJ Beesley","year":"2021","unstructured":"Beesley LJ, Taylor JMG (2021) A stacked approach for chained equations multiple imputation incorporating the substantive model. Biometrics 77(4):1342\u20131354","journal-title":"Biometrics"},{"issue":"48","key":"859_CR16","doi-asserted-by":"publisher","first-page":"57204","DOI":"10.1021\/acsami.1c17378","volume":"13","author":"R Jaafreh","year":"2021","unstructured":"Jaafreh R, Kang YS, Hamad K (2021) Lattice thermal conductivity: an accelerated discovery guided by machine learning. ACS Appl Mater Interfaces 13(48):57204\u201357213","journal-title":"ACS Appl Mater Interfaces"},{"issue":"1","key":"859_CR17","doi-asserted-by":"publisher","DOI":"10.1080\/21505594.2025.2481503","volume":"16","author":"Y Wang","year":"2025","unstructured":"Wang Y, Zheng S, Guo R et al (2025) Assessment for antibiotic resistance in Helicobacter pylori: a practical and interpretable machine learning model based on genome-wide genetic variation. Virulence 16(1):2481503","journal-title":"Virulence"},{"issue":"1","key":"859_CR18","doi-asserted-by":"publisher","first-page":"449","DOI":"10.3233\/JAD-220776","volume":"91","author":"WY Tan","year":"2023","unstructured":"Tan WY, Hargreaves C, Chen C et al (2023) A machine learning approach for early diagnosis of cognitive impairment using population-based data. J Alzheimers Dis 91(1):449\u2013461","journal-title":"J Alzheimers Dis"},{"issue":"12","key":"859_CR19","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1016\/j.jamda.2022.09.002","volume":"23","author":"J Shi","year":"2022","unstructured":"Shi J, He Q, Pan Y et al (2022) Estimation of appendicular skeletal muscle mass for women aged 60\u201370 years using a machine learning approach. J Am Med Dir Assoc 23(12):1981\u20131985","journal-title":"J Am Med Dir Assoc"},{"issue":"1","key":"859_CR20","doi-asserted-by":"publisher","first-page":"20875","DOI":"10.1038\/s41598-024-71548-3","volume":"14","author":"H Xu","year":"2024","unstructured":"Xu H, Ma Y, Zhuang Y et al (2024) Machine learning-based risk prediction model construction of difficult weaning in ICU patients with mechanical ventilation. Sci Rep 14(1):20875","journal-title":"Sci Rep"},{"issue":"1","key":"859_CR21","doi-asserted-by":"publisher","DOI":"10.1186\/s12933-024-02542-2","volume":"23","author":"J Lin","year":"2024","unstructured":"Lin J, Chen Y, Xu M et al (2024) Association and predictive ability between significant perioperative cardiovascular adverse events and stress glucose rise in patients undergoing non-cardiac surgery. Cardiovasc Diabetol 23(1):445","journal-title":"Cardiovasc Diabetol"},{"issue":"6","key":"859_CR22","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1177\/1745691617693393","volume":"12","author":"T Yarkoni","year":"2017","unstructured":"Yarkoni T, Westfall J (2017) Choosing prediction over explanation in psychology: lessons from machine learning. Perspect Psychol Sci 12(6):1100\u20131122","journal-title":"Perspect Psychol Sci"},{"issue":"1","key":"859_CR23","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s12028-008-9059-x","volume":"9","author":"MC Vidotto","year":"2008","unstructured":"Vidotto MC, Sogame LCM, Calciolari CC et al (2008) The prediction of extubation success of postoperative neurosurgical patients using frequency-tidal volume ratios. Neurocrit Care 9(1):83\u201389","journal-title":"Neurocrit Care"},{"issue":"4","key":"859_CR24","doi-asserted-by":"publisher","first-page":"415","DOI":"10.4046\/trd.2024.0039","volume":"87","author":"TS Ha","year":"2024","unstructured":"Ha TS, Oh DK, Lee H et al (2024) Liberation from mechanical ventilation in critically Ill patients: Korean Society of Critical Care Medicine Clinical Practice Guidelines. Tuberc Respir Dis 87(4):415\u2013439","journal-title":"Tuberc Respir Dis"},{"issue":"1","key":"859_CR25","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-021-03802-3","volume":"25","author":"F Torrini","year":"2021","unstructured":"Torrini F, Gendreau S, Morel J et al (2021) Prediction of extubation outcome in critically ill patients: a systematic review and meta-analysis. Crit Care 25(1):391","journal-title":"Crit Care"},{"key":"859_CR26","doi-asserted-by":"crossref","unstructured":"Fritsch SJ, Dreher M, Simon T, et al. (2022) Haemoglobin value and red blood cell transfusions in prolonged weaning from mechanical ventilation: a retrospective observational study. BMJ Open Respir Res. 9 1.","DOI":"10.1136\/bmjresp-2022-001228"},{"issue":"4","key":"859_CR27","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1164\/rccm.201011-1887OC","volume":"184","author":"O Penuelas","year":"2011","unstructured":"Penuelas O, Frutos-Vivar F, Fernandez C et al (2011) Characteristics and outcomes of ventilated patients according to time to liberation from mechanical ventilation. Am J Respir Crit Care Med 184(4):430\u2013437","journal-title":"Am J Respir Crit Care Med"},{"issue":"5","key":"859_CR28","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1272\/jnms.JNMS.2021_88-508","volume":"88","author":"T Otaguro","year":"2021","unstructured":"Otaguro T, Tanaka H, Igarashi Y et al (2021) Machine learning for prediction of successful extubation of mechanical ventilated patients in an intensive care unit: a retrospective observational study. J Nippon Med Sch 88(5):408\u2013417","journal-title":"J Nippon Med Sch"},{"key":"859_CR29","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.663739","volume":"8","author":"M Lin","year":"2021","unstructured":"Lin M, Li C, Lin P et al (2021) Explainable machine learning to predict successful weaning among patients requiring prolonged mechanical ventilation: a retrospective cohort study in Central Taiwan. Front Med 8:663739","journal-title":"Front Med"},{"issue":"1","key":"859_CR30","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.chest.2016.10.036","volume":"151","author":"DR Ouellette","year":"2017","unstructured":"Ouellette DR, Patel S, Girard TD et al (2017) Liberation from mechanical ventilation in critically ill adults: an official american college of chest physicians\/american thoracic society clinical practice guideline: inspiratory pressure augmentation during spontaneous breathing trials, protocols minimizing sedation, and noninvasive ventilation immediately after extubation. Chest 151(1):166\u2013180","journal-title":"Chest"},{"issue":"1","key":"859_CR31","doi-asserted-by":"publisher","DOI":"10.1186\/s12890-022-02096-7","volume":"22","author":"H Wang","year":"2022","unstructured":"Wang H, Zhao Q, Luo J et al (2022) Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model. BMC Pulm Med 22(1):304","journal-title":"BMC Pulm Med"},{"key":"859_CR32","doi-asserted-by":"publisher","DOI":"10.2196\/44763","volume":"7","author":"J Kim","year":"2023","unstructured":"Kim J, Kim YK, Kim H et al (2023) Machine learning algorithms predict successful weaning from mechanical ventilation before intubation: retrospective analysis from the medical information mart for intensive care IV database. JMIR Form Res 7:e44763","journal-title":"JMIR Form Res"},{"issue":"96","key":"859_CR33","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.dss.2017.01.007","volume":"1","author":"Y Gao","year":"2017","unstructured":"Gao Y, Xu A, Hu PJ, Cheng TH (2017) Incorporating association rule networks in feature category-weighted naive Bayes model to support weaning decision making. Decis Support Syst 1(96):27\u201338","journal-title":"Decis Support Syst"},{"key":"859_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101822","volume":"104","author":"J Waring","year":"2020","unstructured":"Waring J, Lindvall C, Umeton R (2020) Automated machine learning: review of the state-of-the-art and opportunities for healthcare. Artif Intell Med 104:101822","journal-title":"Artif Intell Med"},{"key":"859_CR35","doi-asserted-by":"crossref","unstructured":"Cai Z, Huang H, Sun G, et al. (2023) Advancing Predictive Models: Unveiling LightGBM Machine Learning for Data AnalysisC. 2023. IEEE. \u201301\u201301.","DOI":"10.1109\/ICCBD-AI62252.2023.00027"},{"issue":"9","key":"859_CR36","doi-asserted-by":"publisher","first-page":"3227","DOI":"10.3390\/app10093227","volume":"10","author":"Y Wang","year":"2020","unstructured":"Wang Y, Wang T (2020) Application of improved LightGBM model in blood glucose prediction. Appl Sci 10(9):3227","journal-title":"Appl Sci"},{"issue":"2","key":"859_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.redox.2024.103470","volume":"79","author":"X Qi","year":"2025","unstructured":"Qi X, Wang S, Fang C et al (2025) Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants. Redox Biol 79(2):103470","journal-title":"Redox Biol"},{"key":"859_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104813","volume":"137","author":"K Wang","year":"2021","unstructured":"Wang K, Tian J, Zheng C et al (2021) Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP. Comput Biol Med 137:104813","journal-title":"Comput Biol Med"},{"key":"859_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.rmed.2021.106503","volume":"185","author":"B Er","year":"2021","unstructured":"Er B, Simsek M, Yildirim M et al (2021) Association of baseline diaphragm, rectus femoris and vastus intermedius muscle thickness with weaning from mechanical ventilation[J]. Respir Med 185:106503","journal-title":"Respir Med"},{"issue":"1","key":"859_CR40","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.aucc.2016.03.004","volume":"30","author":"S Farghaly","year":"2017","unstructured":"Farghaly S, Hasan AA (2017) Diaphragm ultrasound as a new method to predict extubation outcome in mechanically ventilated patients. Aust Crit Care 30(1):37\u201343","journal-title":"Aust Crit Care"},{"issue":"4","key":"859_CR41","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1007\/s00134-015-3724-2","volume":"41","author":"EC Goligher","year":"2015","unstructured":"Goligher EC, Laghi F, Detsky ME et al (2015) Measuring diaphragm thickness with ultrasound in mechanically ventilated patients: feasibility, reproducibility and validity. Intensive Care Med 41(4):734","journal-title":"Intensive Care Med"},{"issue":"1","key":"859_CR42","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1186\/s13054-015-0894-9","volume":"19","author":"M Umbrello","year":"2015","unstructured":"Umbrello M, Formenti P, Longhi D et al (2015) Diaphragm ultrasound as indicator of respiratory effort in critically ill patients undergoing assisted mechanical ventilation: a pilot clinical study. Crit Care 19(1):161","journal-title":"Crit Care"},{"issue":"10","key":"859_CR43","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.1007\/s00134-022-06808-9","volume":"48","author":"D De Backer","year":"2022","unstructured":"De Backer D, Aissaoui N, Cecconi M et al (2022) How can assessing hemodynamics help to assess volume status? Intensive Care Med 48(10):1482\u20131494","journal-title":"Intensive Care Med"},{"issue":"12","key":"859_CR44","doi-asserted-by":"publisher","first-page":"1542","DOI":"10.4187\/respcare.10159","volume":"67","author":"EN Baedorf Kassis","year":"2022","unstructured":"Baedorf Kassis EN, Bastos AB, Schaefer MS et al (2022) Adaptive support ventilation and lung-protective ventilation in ARDS. Respir Care 67(12):1542\u20131550","journal-title":"Respir Care"}],"container-title":["Intensive Care Medicine Experimental"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40635-026-00859-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40635-026-00859-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40635-026-00859-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T06:04:48Z","timestamp":1768975488000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40635-026-00859-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,21]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["859"],"URL":"https:\/\/doi.org\/10.1186\/s40635-026-00859-8","relation":{},"ISSN":["2197-425X"],"issn-type":[{"value":"2197-425X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,21]]},"assertion":[{"value":"29 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study is a retrospective study in which only patient data were collected for analysis. It did not involve treatment, pathological specimens, or laboratory tests and did not interfere with the treatment of patients. This study was approved by the hospital\u2019s ethics committee (Ethics Committee of Shanghai Tenth People\u2019s Hospital); the batch number was 24K181. 1. The authors confirmed that all the experimental protocols were approved by a named institutional and\/or licensing committee. 2. We confirmed that all methods were carried out in accordance with the relevant guidelines and regulations (Declaration of Helsinki). 3. Informed consent was obtained from all the participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}