{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T14:42:14Z","timestamp":1786372934833,"version":"build-2736575974"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"},{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"funder":[{"name":"GSK"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Drug Saf"],"DOI":"10.1007\/s40264-026-01696-0","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T13:40:05Z","timestamp":1786369205000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimising Pharmacovigilance Efficiency with MLIT (Machine Learning for Intelligent Triage): A Tool for Statistical Safety Alerts"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5468-1021","authenticated-orcid":false,"given":"Luciano","family":"Ciccarelli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9361-3118","authenticated-orcid":false,"given":"Olivia","family":"Mahaux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5670-2767","authenticated-orcid":false,"given":"Christie","family":"Roshan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3976-527X","authenticated-orcid":false,"given":"Ami","family":"Fofana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1331-9528","authenticated-orcid":false,"given":"Anna","family":"Kawka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6621-5913","authenticated-orcid":false,"given":"Emilia","family":"Occhipinti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2784-1573","authenticated-orcid":false,"given":"Mariapia","family":"Possidente","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4413-4468","authenticated-orcid":false,"given":"Silvia","family":"Cenci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9651-9904","authenticated-orcid":false,"given":"Jeffery L.","family":"Painter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3151-3653","authenticated-orcid":false,"given":"Andrew","family":"Bate","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"1696_CR1","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1038\/sj.clpt.6100258","volume":"82","author":"JS Almenoff","year":"2007","unstructured":"Almenoff JS, Pattishall EN, Gibbs TG, DuMouchel W, Evans SJW, Yuen N. Novel statistical tools for monitoring the safety of marketed drugs. Clin Pharmacol Ther. 2007;82:157\u201366. https:\/\/doi.org\/10.1038\/sj.clpt.6100258.","journal-title":"Clin Pharmacol Ther"},{"issue":"Suppl 1","key":"1696_CR2","doi-asserted-by":"publisher","first-page":"S15","DOI":"10.1002\/(SICI)1099-1557(199904)8:1+<S15::AID-PDS402>3.0.CO;2-B","volume":"8","author":"M Lindquist","year":"1999","unstructured":"Lindquist M, Edwards IR, Bate A, Fucik H, Nunes AM, St\u00e5hl M. From association to alert-a revised approach to international signal analysis. Pharmacoepidemiol Drug Saf. 1999;8(Suppl 1):S15\u201325.\u00a0https:\/\/doi.org\/10.1002\/(SICI)1099-1557(199904)8:1+<S15::AID-PDS402>3.0.CO;2-B.","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"1696_CR3","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1002\/pds.1742","volume":"18","author":"A Bate","year":"2009","unstructured":"Bate A, Evans SJW. Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiol Drug Saf. 2009;18:427\u201336. https:\/\/doi.org\/10.1002\/pds.1742.","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"1696_CR4","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1038\/clpt.2013.24","volume":"93","author":"R Harpaz","year":"2013","unstructured":"Harpaz R, DuMouchel W, LePendu P, Bauer-Mehren A, Ryan P, Shah NH. Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clin Pharmacol Ther. 2013;93:539\u201346. https:\/\/doi.org\/10.1038\/clpt.2013.24.","journal-title":"Clin Pharmacol Ther"},{"key":"1696_CR5","unstructured":"CIOMS Working Group VIII. Practical Aspects of Signal Detection in Pharmacovigilance. 2010. https:\/\/cioms.ch\/wp-content\/uploads\/2018\/03\/WG8-Signal-Detection.pdf. Accessed 19 Jan 2026."},{"key":"1696_CR6","doi-asserted-by":"publisher","first-page":"99","DOI":"10.2165\/00002018-200932020-00003","volume":"32","author":"M Hauben","year":"2009","unstructured":"Hauben M, Aronson JK. Defining \u201csignal\u201d and its subtypes in pharmacovigilance based on a systematic review of previous definitions. Drug Saf. 2009;32:99\u2013110. https:\/\/doi.org\/10.2165\/00002018-200932020-00003.","journal-title":"Drug Saf"},{"key":"1696_CR7","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1111\/j.1742-7843.2006.pto_232.x","volume":"98","author":"A Bate","year":"2006","unstructured":"Bate A, Edwards IR. Data mining in spontaneous reports. Basic Clin Pharmacol Toxicol. 2006;98:324\u201330. https:\/\/doi.org\/10.1111\/j.1742-7843.2006.pto_232.x.","journal-title":"Basic Clin Pharmacol Toxicol"},{"key":"1696_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fdsfr.2023.1323057","volume":"3","author":"PM Cutroneo","year":"2023","unstructured":"Cutroneo PM, Sartori D, Tuccori M, Crisafulli S, Battini V, Carnovale C, et al. Conducting and interpreting disproportionality analyses derived from spontaneous reporting systems. Front Drug Saf Regul. 2023;3:1323057. https:\/\/doi.org\/10.3389\/fdsfr.2023.1323057.","journal-title":"Front Drug Saf Regul"},{"key":"1696_CR9","doi-asserted-by":"publisher","DOI":"10.3389\/fdsfr.2023.1110498","volume":"3","author":"JL Painter","year":"2023","unstructured":"Painter JL, Kassekert R, Bate A. An industry perspective on the use of machine learning in drug and vaccine safety. Front Drug Saf Regul. 2023;3:1110498. https:\/\/doi.org\/10.3389\/fdsfr.2023.1110498.","journal-title":"Front Drug Saf Regul"},{"key":"1696_CR10","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s40290-025-00561-2","volume":"39","author":"J Warner","year":"2025","unstructured":"Warner J, Prada Jardim A, Albera C. Artificial intelligence: applications in pharmacovigilance signal management. Pharmaceut Med. 2025;39:183\u201398. https:\/\/doi.org\/10.1007\/s40290-025-00561-2.","journal-title":"Pharmaceut Med"},{"key":"1696_CR11","doi-asserted-by":"publisher","first-page":"204209862514127","DOI":"10.1177\/20420986251412773","volume":"17","author":"A Bate","year":"2026","unstructured":"Bate A, Michael Tregunno P. How is AI developing in pharmacovigilance? Ther Adv Drug Saf. 2026;17:20420986251412773. https:\/\/doi.org\/10.1177\/20420986251412773.","journal-title":"Ther Adv Drug Saf."},{"key":"1696_CR12","doi-asserted-by":"publisher","first-page":"204209862412933","DOI":"10.1177\/20420986241293303","volume":"15","author":"M Glaser","year":"2024","unstructured":"Glaser M, Littlebury R. Governance of artificial intelligence and machine learning in pharmacovigilance: what works today and what more is needed? Ther Adv Drug Saf. 2024;15:20420986241293303. https:\/\/doi.org\/10.1177\/20420986241293303.","journal-title":"Ther Adv Drug Saf."},{"key":"1696_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.hlpt.2023.100743","volume":"12","author":"A Bate","year":"2023","unstructured":"Bate A, Stegmann J-U. Artificial intelligence and pharmacovigilance: what is happening, what could happen and what should happen? Health Policy Technol. 2023;12:100743. https:\/\/doi.org\/10.1016\/j.hlpt.2023.100743.","journal-title":"Health Policy Technol"},{"key":"1696_CR14","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s40264-013-0053-7","volume":"36","author":"J Strandell","year":"2013","unstructured":"Strandell J, Caster O, Hopstadius J, Edwards IR, Nor\u00e9n GN. The development and evaluation of triage algorithms for early discovery of adverse drug interactions. Drug Saf. 2013;36:371\u201388. https:\/\/doi.org\/10.1007\/s40264-013-0053-7.","journal-title":"Drug Saf"},{"key":"1696_CR15","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1002\/sam.10078","volume":"3","author":"O Caster","year":"2010","unstructured":"Caster O, Nor\u00e9n GN, Madigan D, Bate A. Large\u2010scale regression\u2010based pattern discovery: the example of screening the WHO global drug safety database. Stat Anal Data Min. 2010;3:197\u2013208. https:\/\/doi.org\/10.1002\/sam.10078.","journal-title":"Stat Anal Data Min"},{"key":"1696_CR16","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1007\/s40264-023-01317-0","volume":"46","author":"F Al-Azzawi","year":"2023","unstructured":"Al-Azzawi F, Mahmoud I, Haguinet F, Bate A, Sessa M. Developing an artificial intelligence-guided signal detection in the Food and Drug Administration Adverse Event Reporting System (FAERS): a proof-of-concept study using galcanezumab and simulated data. Drug Saf. 2023;46:743\u201351. https:\/\/doi.org\/10.1007\/s40264-023-01317-0.","journal-title":"Drug Saf"},{"key":"1696_CR17","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2020.602365","volume":"11","author":"J-H Bae","year":"2021","unstructured":"Bae J-H, Baek Y-H, Lee J-E, Song I, Lee J-H, Shin J-Y. Machine learning for detection of safety signals from spontaneous reporting system data: example of Nivolumab and Docetaxel. Front Pharmacol. 2021;11:602365. https:\/\/doi.org\/10.3389\/fphar.2020.602365.","journal-title":"Front Pharmacol"},{"key":"1696_CR18","doi-asserted-by":"publisher","first-page":"204209862312194","DOI":"10.1177\/20420986231219472","volume":"14","author":"DG Dauner","year":"2023","unstructured":"Dauner DG, Leal E, Adam TJ, Zhang R, Farley JF. Evaluation of four machine learning models for signal detection. Ther Adv Drug Saf. 2023;14:20420986231219472. https:\/\/doi.org\/10.1177\/20420986231219472.","journal-title":"Ther Adv Drug Saf."},{"key":"1696_CR19","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1007\/s40264-018-00792-0","volume":"42","author":"M Pham","year":"2019","unstructured":"Pham M, Cheng F, Ramachandran K. A comparison study of algorithms to detect drug-adverse event associations: frequentist, Bayesian, and machine-learning approaches. Drug Saf. 2019;42:743\u201350. https:\/\/doi.org\/10.1007\/s40264-018-00792-0.","journal-title":"Drug Saf"},{"key":"1696_CR20","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s40264-023-01381-6","volume":"47","author":"G Dong","year":"2024","unstructured":"Dong G, Bate A, Haguinet F, Westman G, D\u00fcrlich L, Hviid A, et al. Optimizing signal management in a vaccine adverse event reporting system: a proof-of-concept with COVID-19 vaccines using signs, symptoms, and natural language processing. Drug Saf. 2024;47:173\u201382. https:\/\/doi.org\/10.1007\/s40264-023-01381-6.","journal-title":"Drug Saf"},{"key":"1696_CR21","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/s40264-022-01159-2","volume":"45","author":"M Imran","year":"2022","unstructured":"Imran M, Bhatti A, King DM, Lerch M, Dietrich J, Doron G, et al. Supervised machine learning-based decision support for signal validation classification. Drug Saf. 2022;45:583\u201396. https:\/\/doi.org\/10.1007\/s40264-022-01159-2.","journal-title":"Drug Saf"},{"key":"1696_CR22","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s40264-020-01001-7","volume":"44","author":"A Bate","year":"2021","unstructured":"Bate A, Hobbiger SF. Artificial intelligence, real-world automation and the safety of medicines. Drug Saf. 2021;44:125\u201332. https:\/\/doi.org\/10.1007\/s40264-020-01001-7.","journal-title":"Drug Saf"},{"key":"1696_CR23","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1007\/s40264-023-01327-y","volume":"46","author":"V Kara","year":"2023","unstructured":"Kara V, Powell G, Mahaux O, Jayachandra A, Nyako N, Golds C, et al. Finding needles in the haystack: clinical utility score for prioritisation (CUSP), an automated approach for identifying spontaneous reports with the highest clinical utility. Drug Saf. 2023;46:847\u201355. https:\/\/doi.org\/10.1007\/s40264-023-01327-y.","journal-title":"Drug Saf"},{"key":"1696_CR24","doi-asserted-by":"publisher","first-page":"381","DOI":"10.2165\/00002018-200225060-00001","volume":"25","author":"A Szarfman","year":"2002","unstructured":"Szarfman A, Machado SG, O\u2019Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA\u2019s spontaneous reports database. Drug Saf. 2002;25:381\u201392. https:\/\/doi.org\/10.2165\/00002018-200225060-00001.","journal-title":"Drug Saf"},{"key":"1696_CR25","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1002\/pds.3226","volume":"21","author":"L Van Holle","year":"2012","unstructured":"Van Holle L, Zeinoun Z, Bauchau V, Verstraeten T. Using time-to-onset for detecting safety signals in spontaneous reports of adverse events following immunization: a proof of concept study. Pharmacoepidemiol Drug Saf. 2012;21:603\u201310. https:\/\/doi.org\/10.1002\/pds.3226.","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"1696_CR26","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1002\/pds.4344","volume":"27","author":"LC Pinheiro","year":"2018","unstructured":"Pinheiro LC, Candore G, Zaccaria C, Slattery J, Arlett P. An algorithm to detect unexpected increases in frequency of reports of adverse events in EudraVigilance. Pharmacoepidemiol Drug Saf. 2018;27:38\u201345. https:\/\/doi.org\/10.1002\/pds.4344.","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"1696_CR27","unstructured":"Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for text classification. Advances in neural information processing systems (NeurIPS 2015), Canada, 7\u201312 December 2015, 649\u201357."},{"key":"1696_CR28","doi-asserted-by":"crossref","unstructured":"Navada A, Ansari AN, Patil S, Sonkamble BA. Overview of use of decision tree algorithms in machine learning. In: IEEE Control and System Graduate Research Colloquium; IEEE, Malaysia, 2011; pp. 37\u201342.","DOI":"10.1109\/ICSGRC.2011.5991826"},{"key":"1696_CR29","doi-asserted-by":"publisher","first-page":"218","DOI":"10.21037\/atm.2016.03.37","volume":"4","author":"Z Zhang","year":"2016","unstructured":"Zhang Z. Introduction to machine learning: k-nearest neighbors. Ann Transl Med. 2016;4:218. https:\/\/doi.org\/10.21037\/atm.2016.03.37.","journal-title":"Ann Transl Med"},{"key":"1696_CR30","doi-asserted-by":"crossref","unstructured":"Wang Q. Support vector machine algorithm in machine learning. In: IEEE International Conference on artificial intelligence and computer applications (ICAICA); IEEE, China, 2022; pp. 750\u20136.","DOI":"10.1109\/ICAICA54878.2022.9844516"},{"key":"1696_CR31","doi-asserted-by":"publisher","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","volume":"47","author":"SJ Rigatti","year":"2017","unstructured":"Rigatti SJ. Random forest. J Insur Med. 2017;47:31\u20139. https:\/\/doi.org\/10.17849\/insm-47-01-31-39.1.","journal-title":"J Insur Med"},{"key":"1696_CR32","unstructured":"Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, et al. Xgboost: extreme gradient boosting. R package version 04-2. 2015;1:1\u20134. https:\/\/github.com\/dmlc\/xgboost."},{"key":"1696_CR33","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA: Association for Computing Machinery; 2016. pp. 785\u201394. https:\/\/doi.org\/10.1145\/2939672.785.","DOI":"10.1145\/2939672.785"},{"key":"1696_CR34","first-page":"2825","volume":"12","author":"F Prodregosa","year":"2011","unstructured":"Prodregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"1696_CR35","unstructured":"Liang J. Confusion matrix: Machine learning. POGIL Act Clear. 2022;3. Retrieved from https:\/\/pac.pogil.org\/index.php\/pac\/article\/view\/304."},{"key":"1696_CR36","doi-asserted-by":"publisher","DOI":"10.1111\/cts.70056","volume":"17","author":"AV Ponce-Bobadilla","year":"2024","unstructured":"Ponce-Bobadilla AV, Schmitt V, Maier CS, Mensing S, Stodtmann S. Practical guide to SHAP analysis: explaining supervised machine learning model predictions in drug development. Clin Transl Sci. 2024;17:e70056. https:\/\/doi.org\/10.1111\/cts.70056.","journal-title":"Clin Transl Sci"},{"key":"1696_CR37","doi-asserted-by":"publisher","unstructured":"Painter JL, Haguinet F, Powell GE, Bate A. Ontology-based semantic similarity measures for clustering medical concepts in drug safety. AMIA Annu Symp Proc. 2024;2024:979\u201388. https:\/\/doi.org\/10.48550\/arXiv.2503.20737.","DOI":"10.48550\/arXiv.2503.20737"},{"key":"1696_CR38","unstructured":"CIOMS Working Group XIV. Artificial Intelligence in Pharmacovigilance. 2025. https:\/\/cioms.ch\/wp-content\/uploads\/2022\/05\/CIOMS-WG-XIV_Draft-report-for-Public-Consultation_1May2025.pdf. Accessed 19 Jan 2026."},{"key":"1696_CR39","doi-asserted-by":"publisher","first-page":"204209862513960","DOI":"10.1177\/20420986251396023","volume":"16","author":"D Ramcharran","year":"2025","unstructured":"Ramcharran D, Painter JL, Kara V, Glaser M, Vanini M, Chalamalasetti VR, et al. Orchestrating generative AI in pharmacovigilance: predicting and preempting the unpredictable. Ther Adv Drug Saf. 2025;16:20420986251396023. https:\/\/doi.org\/10.1177\/20420986251396023.","journal-title":"Ther Adv Drug Saf."}],"container-title":["Drug Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40264-026-01696-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40264-026-01696-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40264-026-01696-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T13:40:10Z","timestamp":1786369210000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40264-026-01696-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,10]]},"references-count":39,"alternative-id":["1696"],"URL":"https:\/\/doi.org\/10.1007\/s40264-026-01696-0","relation":{},"ISSN":["0114-5916","1179-1942"],"issn-type":[{"value":"0114-5916","type":"print"},{"value":"1179-1942","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,10]]},"assertion":[{"value":"13 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All authors are\/were employed by GSK at the time of study. OM, CR, SC, JLP, and AB hold financial equities in GSK. AB is an Editorial Board member of\n                      Drug Safety\n                      . AB was not involved in the selection of peer reviewers for the manuscript nor in any of the subsequent editorial decisions. The authors declare no other financial or non-financial relationships and activities.","order":1,"name":"Ethics","label":"Conflict of Interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":4,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The data that support the findings of this study are available from the corresponding author (LC), upon reasonable request.","order":5,"name":"Ethics","label":"Availability of Data and Material","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":6,"name":"Ethics","label":"Code Availability","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"LC, OM, CR, SC, JLP, and AB participated in study concept\/design; OM was involved in data acquisition; LC, OM, CR, AF, AK, EO, and MP participated to data analysis. All authors took part in data interpretation. All authors read and approved the final version.\u00a0","order":7,"name":"Ethics","label":"Authors\u2019 Contributions","group":{"name":"EthicsHeading","label":"Declarations"}}]}}