{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T18:06:29Z","timestamp":1788717989331,"version":"build-2803163510"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T00:00:00Z","timestamp":1707868800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T00:00:00Z","timestamp":1707868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diagnostic codes are commonly used as inputs for clinical prediction models, to create labels for prediction tasks, and to identify cohorts for multicenter network studies. However, the coverage rates of diagnostic codes and their variability across institutions are underexplored. The primary objective was to describe lab- and diagnosis-based labels for 7 selected outcomes at three institutions. Secondary objectives were to describe agreement, sensitivity, and specificity of diagnosis-based labels against lab-based labels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included three cohorts: SickKids from The Hospital for Sick Children, and StanfordPeds and StanfordAdults from Stanford Medicine. We included seven clinical outcomes with lab-based definitions: acute kidney injury, hyperkalemia, hypoglycemia, hyponatremia, anemia, neutropenia and thrombocytopenia. For each outcome, we created four lab-based labels (abnormal, mild, moderate and severe) based on test result and one diagnosis-based label. Proportion of admissions with a positive label were presented for each outcome stratified by cohort. Using lab-based labels as the gold standard, agreement using Cohen\u2019s Kappa, sensitivity and specificity were calculated for each lab-based severity level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The number of admissions included were: SickKids (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200959,298), StanfordPeds (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200924,639) and StanfordAdults (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009159,985). The proportion of admissions with a positive diagnosis-based label was significantly higher for StanfordPeds compared to SickKids across all outcomes, with odds ratio (99.9% confidence interval) for abnormal diagnosis-based label ranging from 2.2 (1.7\u20132.7) for neutropenia to 18.4 (10.1\u201333.4) for hyperkalemia. Lab-based labels were more similar by institution. When using lab-based labels as the gold standard, Cohen\u2019s Kappa and sensitivity were lower at SickKids for all severity levels compared to StanfordPeds.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Across multiple outcomes, diagnosis codes were consistently different between the two pediatric institutions. This difference was not explained by differences in test results. These results may have implications for machine learning model development and deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-024-02449-8","type":"journal-article","created":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T08:02:34Z","timestamp":1707897754000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare"],"prefix":"10.1186","volume":"24","author":[{"given":"Lin Lawrence","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keith E.","family":"Morse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine","family":"Aftandilian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ethan","family":"Steinberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Fries","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose","family":"Posada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott Lanyon","family":"Fleming","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Lemmon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karim","family":"Jessa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nigam","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lillian","family":"Sung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,14]]},"reference":[{"issue":"31","key":"2449_CR1","doi-asserted-by":"publisher","first-page":"3652","DOI":"10.1200\/JCO.20.01688","volume":"38","author":"JC Hong","year":"2020","unstructured":"Hong JC, Eclov NCW, Dalal NH, Thomas SM, Stephens SJ, Malicki M, et al. System for high-intensity evaluation during Radiation Therapy (SHIELD-RT): a prospective Randomized Study of Machine Learning\u2013Directed clinical evaluations during Radiation and Chemoradiation. J Clin Oncol. 2020;38(31):3652\u201361.","journal-title":"J Clin Oncol"},{"issue":"20","key":"2449_CR2","doi-asserted-by":"publisher","first-page":"1951","DOI":"10.1056\/NEJMsa2001090","volume":"383","author":"GJ Escobar","year":"2020","unstructured":"Escobar GJ, Liu VX, Schuler A, Lawson B, Greene JD, Kipnis P. Automated identification of adults at risk for In-Hospital clinical deterioration. N Engl J Med. 2020;383(20):1951\u201360.","journal-title":"N Engl J Med"},{"issue":"12","key":"2449_CR3","doi-asserted-by":"publisher","first-page":"e204759","DOI":"10.1001\/jamaoncol.2020.4759","volume":"6","author":"CR Manz","year":"2020","unstructured":"Manz CR, Parikh RB, Small DS, Evans CN, Chivers C, Regli SH, et al. Effect of integrating machine learning mortality estimates with behavioral nudges to clinicians on Serious Illness conversations among patients with Cancer: a stepped-Wedge Cluster Randomized Clinical Trial. JAMA Oncol. 2020;6(12):e204759\u2013e.","journal-title":"JAMA Oncol"},{"issue":"43","key":"2449_CR4","doi-asserted-by":"publisher","first-page":"11368","DOI":"10.1073\/pnas.1714097114","volume":"114","author":"L Guan","year":"2017","unstructured":"Guan L, Tian X, Gombar S, Zemek AJ, Krishnan G, Scott R, et al. Big data modeling to predict platelet usage and minimize wastage in a tertiary care system. Proc Natl Acad Sci U S A. 2017;114(43):11368\u201373.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"7","key":"2449_CR5","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1038\/s41591-019-0503-6","volume":"25","author":"I Yelin","year":"2019","unstructured":"Yelin I, Snitser O, Novich G, Katz R, Tal O, Parizade M, et al. Personal clinical history predicts antibiotic resistance of urinary tract infections. Nat Med. 2019;25(7):1143\u201352.","journal-title":"Nat Med"},{"issue":"5 Pt 2","key":"2449_CR6","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1111\/j.1475-6773.2005.00444.x","volume":"40","author":"KJ O\u2019Malley","year":"2005","unstructured":"O\u2019Malley KJ, Cook KF, Price MD, Wildes KR, Hurdle JF, Ashton CM. Measuring diagnoses: ICD code accuracy. Health Serv Res. 2005;40(5 Pt 2):1620\u201339.","journal-title":"Health Serv Res"},{"issue":"1","key":"2449_CR7","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1186\/s12874-017-0361-1","volume":"17","author":"K Burles","year":"2017","unstructured":"Burles K, Innes G, Senior K, Lang E, McRae A. Limitations of pulmonary embolism ICD-10 codes in emergency department administrative data: let the buyer beware. BMC Med Res Methodol. 2017;17(1):89.","journal-title":"BMC Med Res Methodol"},{"issue":"3","key":"2449_CR8","doi-asserted-by":"publisher","first-page":"E617","DOI":"10.9778\/cmajo.20170036","volume":"5","author":"KL Tang","year":"2017","unstructured":"Tang KL, Lucyk K, Quan H. Coder perspectives on physician-related barriers to producing high-quality administrative data: a qualitative study. CMAJ Open. 2017;5(3):E617.","journal-title":"CMAJ Open"},{"key":"2449_CR9","doi-asserted-by":"crossref","unstructured":"Liu B, Hadzi-Tosev M, Liu Y, Lucier KJ, Garg A, Li S et al. Accuracy of International classification of diseases, 10th Revision codes for identifying Sepsis: a systematic review and Meta-analysis. Crit Care Explorations. 2022;4(11).","DOI":"10.1097\/CCE.0000000000000788"},{"issue":"11","key":"2449_CR10","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1212\/01.wnl.0000247281.98094.e2","volume":"67","author":"MR Golomb","year":"2006","unstructured":"Golomb MR, Garg BP, Saha C, Williams LS. Accuracy and yield of ICD-9 codes for identifying children with ischemic stroke. Neurology. 2006;67(11):2053.","journal-title":"Neurology"},{"issue":"11","key":"2449_CR11","doi-asserted-by":"publisher","first-page":"e21586","DOI":"10.1016\/j.heliyon.2023.e21586","volume":"9","author":"LL Guo","year":"2023","unstructured":"Guo LL, Calligan M, Vettese E, Cook S, Gagnidze G, Han O, et al. Development and validation of the SickKids Enterprise-wide data in Azure Repository (SEDAR). Heliyon. 2023;9(11):e21586.","journal-title":"Heliyon"},{"key":"2449_CR12","unstructured":"Guo LL, Calligan M, Vettese E, Cook S, Gagnidze G, Han O et al. Development and validation of the SickKids Enterprise-wide Data in Azure Repository (SEDAR). In Press."},{"key":"2449_CR13","unstructured":"Datta S, Posada J, Olson G, Li W, O\u2019Reilly C, Balraj D et al. A new paradigm for accelerating clinical data science at Stanford Medicine. arXiv preprint arXiv:200310534. 2020."},{"key":"2449_CR14","unstructured":"Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ et al. Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Studies in health technology and informatics. 2015;216:574."},{"issue":"4","key":"2449_CR15","doi-asserted-by":"publisher","first-page":"c179","DOI":"10.1159\/000339789","volume":"120","author":"A Khwaja","year":"2012","unstructured":"Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179\u201384.","journal-title":"Nephron Clin Pract"},{"issue":"6","key":"2449_CR16","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1016\/j.pedhc.2013.08.003","volume":"27","author":"K Daly","year":"2013","unstructured":"Daly K, Farrington E. Hypokalemia and hyperkalemia in infants and children: pathophysiology and treatment. J Pediatr Health Care. 2013;27(6):486\u201396. quiz 97\u2013\u20098.","journal-title":"J Pediatr Health Care"},{"key":"2449_CR17","doi-asserted-by":"crossref","unstructured":"Abraham MB, Jones TW, Naranjo D, Karges B, Oduwole A, Tauschmann M et al. ISPAD Clinical Practice Consensus guidelines 2018: Assessment and management of hypoglycemia in children and adolescents with diabetes. Pediatr Diabetes. 2018;19 Suppl 27:178\u2013\u200992.","DOI":"10.1111\/pedi.12698"},{"issue":"3","key":"2449_CR18","doi-asserted-by":"publisher","first-page":"G1","DOI":"10.1530\/EJE-13-1020","volume":"170","author":"G Spasovski","year":"2014","unstructured":"Spasovski G, Vanholder R, Allolio B, Annane D, Ball S, Bichet D, et al. Clinical practice guideline on diagnosis and treatment of hyponatraemia. Eur J Endocrinol. 2014;170(3):G1\u201347.","journal-title":"Eur J Endocrinol"},{"issue":"11","key":"2449_CR19","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1080\/17474086.2017.1354696","volume":"10","author":"S Allali","year":"2017","unstructured":"Allali S, Brousse V, Sacri AS, Chalumeau M, de Montalembert M. Anemia in children: prevalence, causes, diagnostic work-up, and long-term consequences. Expert Rev Hematol. 2017;10(11):1023\u20138.","journal-title":"Expert Rev Hematol"},{"issue":"12","key":"2449_CR20","first-page":"825","volume":"10","author":"MB Lustberg","year":"2012","unstructured":"Lustberg MB. Management of neutropenia in cancer patients. Clin Adv Hematol Oncol. 2012;10(12):825\u20136.","journal-title":"Clin Adv Hematol Oncol"},{"key":"2449_CR21","unstructured":"Chernecky C, Barbara B. Platelet (thrombocyte) count - blood. Laboratory Tests and Diagnostic Procedures. 6th edition ed. St Louis, MO: Elsevier Saunders; 2013. p. 886-7."},{"issue":"Supplement3","key":"2449_CR22","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1542\/peds.2012-0055I","volume":"129","author":"K Williams","year":"2012","unstructured":"Williams K, Thomson D, Seto I, Contopoulos-Ioannidis DG, Ioannidis JPA, Curtis S, et al. Standard 6: Age groups for Pediatric trials. Pediatrics. 2012;129(Supplement3):153\u2013S60.","journal-title":"Pediatrics"},{"key":"2449_CR23","unstructured":"Tomlinson LA, Riding AM, Payne RA, Abel GA, Tomson CR, Wilkinson IB et al. The accuracy of diagnostic coding for acute kidney injury in England\u2013 a single centre study."},{"issue":"4","key":"2449_CR24","doi-asserted-by":"publisher","first-page":"682","DOI":"10.2215\/CJN.07650713","volume":"9","author":"ME Grams","year":"2014","unstructured":"Grams ME, Waikar SS, MacMahon B, Whelton S, Ballew SH, Coresh J. Performance and Limitations of Administrative Data in the identification of AKI. Clin J Am Soc Nephrol. 2014;9(4):682\u20139.","journal-title":"Clin J Am Soc Nephrol"},{"issue":"8","key":"2449_CR25","doi-asserted-by":"publisher","first-page":"e2017703","DOI":"10.1001\/jamanetworkopen.2020.17703","volume":"3","author":"BT Crabb","year":"2020","unstructured":"Crabb BT, Lyons A, Bale M, Martin V, Berger B, Mann S, et al. Comparison of International Classification of Diseases and related health problems, Tenth Revision codes with Electronic Medical records among patients with symptoms of Coronavirus Disease 2019. JAMA Netw Open. 2020;3(8):e2017703\u2013e.","journal-title":"JAMA Netw Open"},{"issue":"8","key":"2449_CR26","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1093\/jamia\/ocy032","volume":"25","author":"JM Reps","year":"2018","unstructured":"Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J Am Med Inform Assoc. 2018;25(8):969\u201375.","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"2449_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-021-00455-y","volume":"4","author":"L Rasmy","year":"2021","unstructured":"Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit Med. 2021;4(1):1\u201313.","journal-title":"NPJ Digit Med"},{"issue":"1","key":"2449_CR28","doi-asserted-by":"publisher","first-page":"2726","DOI":"10.1038\/s41598-022-06484-1","volume":"12","author":"LL Guo","year":"2022","unstructured":"Guo LL, Pfohl SR, Fries J, Johnson AEW, Posada J, Aftandilian C, et al. Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine. Sci Rep. 2022;12(1):2726.","journal-title":"Sci Rep"},{"key":"2449_CR29","doi-asserted-by":"publisher","first-page":"103637","DOI":"10.1016\/j.jbi.2020.103637","volume":"113","author":"E Steinberg","year":"2021","unstructured":"Steinberg E, Jung K, Fries JA, Corbin CK, Pfohl SR, Shah NH. Language models are an effective representation learning technique for electronic health record data. J Biomed Inform. 2021;113:103637.","journal-title":"J Biomed Inform"},{"issue":"12","key":"2449_CR30","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1093\/jamia\/ocaa139","volume":"27","author":"S Tang","year":"2020","unstructured":"Tang S, Davarmanesh P, Song Y, Koutra D, Sjoding MW, Wiens J. Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. J Am Med Inform Assoc. 2020;27(12):1921\u201334.","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"2449_CR31","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1038\/s41597-019-0103-9","volume":"6","author":"H Harutyunyan","year":"2019","unstructured":"Harutyunyan H, Khachatrian H, Kale DC, Ver Steeg G, Galstyan A. Multitask learning and benchmarking with clinical time series data. Sci Data. 2019;6(1):96.","journal-title":"Sci Data"},{"issue":"6","key":"2449_CR32","doi-asserted-by":"publisher","first-page":"e057977","DOI":"10.1136\/bmjopen-2021-057977","volume":"12","author":"R Khera","year":"2022","unstructured":"Khera R, Schuemie MJ, Lu Y, Ostropolets A, Chen R, Hripcsak G, et al. Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies. BMJ Open. 2022;12(6):e057977.","journal-title":"BMJ Open"},{"issue":"6","key":"2449_CR33","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1080\/10408363.2017.1379945","volume":"54","author":"K Adeli","year":"2017","unstructured":"Adeli K, Higgins V, Trajcevski K, White-Al Habeeb N. The Canadian laboratory initiative on pediatric reference intervals: a CALIPER white paper. Crit Rev Clin Lab Sci. 2017;54(6):358\u2013413.","journal-title":"Crit Rev Clin Lab Sci"},{"key":"2449_CR34","doi-asserted-by":"publisher","first-page":"103621","DOI":"10.1016\/j.jbi.2020.103621","volume":"113","author":"SR Pfohl","year":"2021","unstructured":"Pfohl SR, Foryciarz A, Shah NH. An empirical characterization of fair machine learning for clinical risk prediction. J Biomed Inform. 2021;113:103621.","journal-title":"J Biomed Inform"},{"issue":"1","key":"2449_CR35","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1136\/amiajnl-2012-001145","volume":"20","author":"G Hripcsak","year":"2013","unstructured":"Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inf Assoc. 2013;20(1):117\u201321.","journal-title":"J Am Med Inf Assoc"},{"issue":"e1","key":"2449_CR36","doi-asserted-by":"publisher","first-page":"e20","DOI":"10.1093\/jamia\/ocv130","volume":"23","author":"WQ Wei","year":"2016","unstructured":"Wei WQ, Teixeira PL, Mo H, Cronin RM, Warner JL, Denny JC. Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance. J Am Med Inf Assoc. 2016;23(e1):e20\u20137.","journal-title":"J Am Med Inf Assoc"},{"issue":"04","key":"2449_CR37","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1055\/s-0041-1735184","volume":"12","author":"LL Guo","year":"2021","unstructured":"Guo LL, Pfohl SR, Fries J, Posada J, Fleming SL, Aftandilian C, et al. Systematic review of approaches to preserve machine learning performance in the Presence of temporal dataset shift in Clinical Medicine. Appl Clin Inf. 2021;12(04):808\u201315.","journal-title":"Appl Clin Inf"},{"issue":"4","key":"2449_CR38","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1093\/jamiaopen\/ooz035","volume":"2","author":"EF Glynn","year":"2019","unstructured":"Glynn EF, Hoffman MA. Heterogeneity introduced by EHR system implementation in a de-identified data resource from 100 non-affiliated organizations. JAMIA Open. 2019;2(4):554\u201361.","journal-title":"JAMIA Open"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02449-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02449-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02449-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T18:10:15Z","timestamp":1708279815000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02449-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,14]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2449"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02449-8","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.03.14.23287202","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,14]]},"assertion":[{"value":"23 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2024","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 use of data from The Hospital for Sick Children was approved as a quality improvement project at SickKids and thus, requirement for Research Ethics Board approval and informed consent were waived by The Hospital for Sick Children. The data from Stanford Medicine was de-identified in which protected health information has been redacted. Because of de-identification, the requirement for Institutional Review Board approval and informed consent were waived by Stanford Medicine. All methods were carried out in accordance with relevant guidelines and regulations in the Declaration of Helsinki.","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 no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"51"}}