{"id":"W4416605431","doi":"10.1186/s13063-025-09291-x","title":"Who benefits? Uncovering hidden heterogeneity of treatment effects in adaptive trials using Bayesian methods: a systematic review","year":2025,"lang":"en","type":"article","venue":"Trials","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; SickKids Foundation; Institute for Clinical Evaluative Sciences; Public Health Ontario; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; Groupe canadien de recherche en soins intensifs; National Sanitarium Association","keywords":"Bayesian probability; Clinical trial; Equity (law); Bayes' theorem; Bayesian statistics; MEDLINE; Research design; Bayesian inference","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06316445,0.001955446,0.01124125,0.008416694,0.0008086224,0.005126792,0.003491707,0.003925461,0.004459065],"category_scores_gemma":[0.274063,0.001760297,0.01295438,0.007454258,0.00196276,0.005812741,0.002671578,0.003111985,0.0004537989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004553645,"about_ca_system_score_gemma":0.01706292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004196421,"about_ca_topic_score_gemma":0.009658428,"domain_scores_codex":[0.92919,0.04460398,0.01502421,0.002735786,0.007781733,0.0006643265],"domain_scores_gemma":[0.6574788,0.3126123,0.01871924,0.003285305,0.007265882,0.0006384878],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0004435699,0.00002967717,0.0008775278,0.8314633,0.01717834,0.00006120992,0.0003676748,0.0005021844,0.0001084811,0.001834981,0.001989558,0.1451435],"study_design_scores_gemma":[0.0005124317,0.0001885066,0.001496782,0.9211146,0.05557655,0.0001558251,0.0002206072,0.0004192849,0.0001664386,0.003574752,0.01651911,0.00005524892],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004356182,0.9959169,0.001541834,0.0009210983,0.0001575827,0.0005554181,0.0001352492,0.00001400199,0.0003223885],"genre_scores_gemma":[0.0165496,0.9746926,0.004598513,0.001728835,0.0002900798,0.001873201,0.0001463449,0.00001790607,0.000102927],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9368355,"threshold_uncertainty_score":0.3340496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7178661278879936,"score_gpt":0.6388013046769554,"score_spread":0.07906482321103825,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}