{"id":"W4310566398","doi":"10.1002/sim.9605","title":"Point estimation for adaptive trial designs I: A methodological review","year":2022,"lang":"en","type":"review","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NIHR Cambridge Biomedical Research Centre; Medical Research Council; National Institute for Health and Care Research; Medical Research Council Canada; Department of Health and Social Care; Cancer Research UK; Health and Care Research Wales","keywords":"Estimator; Computer science; Estimation; Type I and type II errors; Point estimation; Point (geometry); Contrast (vision); Set (abstract data type); Econometrics; Statistics; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch"],"domain":"methods","study_design":"not_applicable","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04316554,0.0008117615,0.009406961,0.000336082,0.0001311826,0.00001435012,0.0007981724,0.0005046224,0.005267129],"category_scores_gemma":[0.7542686,0.0005677468,0.0005448496,0.000814416,0.0004838916,0.00003256701,0.0002458019,0.001704881,0.00001869403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005504522,"about_ca_system_score_gemma":0.0005204607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001021064,"about_ca_topic_score_gemma":0.000003994642,"domain_scores_codex":[0.9778895,0.01376591,0.005443167,0.001123923,0.001130129,0.0006473424],"domain_scores_gemma":[0.5717872,0.4252485,0.001896757,0.000736472,0.0001425246,0.0001885907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001218184,0.000131013,4.397882e-9,0.05135963,0.0001238581,0.00004985278,0.00001982608,2.735988e-7,6.346444e-9,0.2590469,0.04281757,0.6452329],"study_design_scores_gemma":[0.00681773,0.002331284,2.10309e-8,0.03373432,0.003268201,0.00001667119,0.00001456374,0.0001693958,1.842985e-8,0.575735,0.3775855,0.0003272774],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[1.095863e-9,0.4829935,0.5069911,0.00009977412,0.001230257,0.007038091,0.001278348,0.00004340432,0.0003255577],"genre_scores_gemma":[1.507586e-9,0.4989441,0.4967474,0.0002347992,0.000460989,0.003247237,0.0001793687,0.00007606356,0.0001100399],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7111031,"threshold_uncertainty_score":0.9996774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9563494966257471,"score_gpt":0.7322871607745378,"score_spread":0.2240623358512093,"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."}}