{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:50:35Z","timestamp":1791960635762,"version":"4.3.4"},"reference-count":19,"publisher":"Wiley","issue":"19","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["physoc.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Physiological Reports"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Comparing a novel physiological measure against an observer\u2010dependent reference standard creates uncertainty when results disagree because errors may arise from either method. We propose ensemble\u2010based label adjudication (EBLA), a computational framework that uses agreement between two imperfect instruments to create a high\u2010confidence training set for adjudicating discordant observations. EBLA was validated using the Wisconsin Breast Cancer Dataset (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009569) with simulated label noise (10%\u201330%). Five classifiers were trained on concordant cases using stratified 5\u2010fold cross\u2010validation. In the primary 15%\/15% noise scenario, majority\u2010vote EBLA improved accuracy from 0.85 to 0.97, sensitivity from 0.87 to 0.98, and specificity from 0.84 to 0.97. Supermajority and unanimity thresholds did not improve accuracy and excluded 1.4% and 2.5% of cases. EBLA consistently outperformed Cleanlab across all noise conditions. Application to 281 paired Mechanical Ventilation Respiratory Distress Observation Scale (MV\u2010RDOS) assessments and inspiratory effort measurements (PmusPTP) from 45 mechanically ventilated patients increased accuracy from 0.81 to 0.86, primarily by improving specificity from 0.88 to 0.94. EBLA provides a transparent, interpretable framework for sensitivity analysis when validating physiological measurements against imperfect observer\u2010dependent reference standards.\n                  <\/jats:p>","DOI":"10.14814\/phy2.71127","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T03:55:41Z","timestamp":1791604541000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ensemble\u2010based label adjudication (\n                    <scp>EBLA<\/scp>\n                    ): A method for validating continuous physiological measures against observer\u2010dependent reference standards"],"prefix":"10.14814","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7754-5816","authenticated-orcid":false,"given":"Guillermo","family":"Gutierrez","sequence":"first","affiliation":[{"name":"The George Washington University  Washington DC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fernando","family":"Papa","sequence":"additional","affiliation":[{"name":"Hitss Argentina  Buenos Aires Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.00598\u20102018"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.202108\u20101857OC"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45014-9_1"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2292894"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40635\u2010022\u201000483\u20102"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10877\u2010024\u201001164\u2010z"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/cc10309"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/annalsats\/aaoag173"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.2307\/2530508"},{"key":"e_1_2_13_12_1","first-page":"21475","volume-title":"Proceedings of the 40th international conference on machine learning","author":"Liu Y.","year":"2023"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12125"},{"key":"e_1_2_13_14_1","first-page":"2825","article-title":"Scikit\u2010learn: Machine learning in python","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2009.02.005"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.3310\/hta11500"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.10315"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.7326\/0003\u20104819\u2010155\u20108\u2010201110180\u201000009"},{"key":"e_1_2_13_19_1","volume-title":"Breast Cancer Wisconsin (Diagnostic) Data Set [Online]","author":"Wolberg W. 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