{"id":"W4412115660","doi":"10.1177/09622802251338409","title":"Health utility adjusted survival: A composite endpoint for clinical trial designs","year":2025,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"Princess Margaret Cancer Foundation","keywords":"Sample size determination; Clinical endpoint; Clinical trial; Survival analysis; Quality of life (healthcare); Medicine; Randomized controlled trial; Statistical power; Statistics; Econometrics; Mathematics; Surgery; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2552054,0.002314241,0.003977601,0.005360176,0.0009393986,0.003493564,0.002379925,0.003350318,0.007213711],"category_scores_gemma":[0.3740005,0.0009116047,0.008604094,0.005109946,0.003227395,0.003412158,0.003860848,0.006350645,0.001299826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002501371,"about_ca_system_score_gemma":0.005375958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004598187,"about_ca_topic_score_gemma":0.0005602316,"domain_scores_codex":[0.634492,0.3301824,0.01278498,0.006318676,0.01523037,0.0009916201],"domain_scores_gemma":[0.6575412,0.2786661,0.02940525,0.02361652,0.009096213,0.001674723],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01261225,0.0008271519,0.01778014,0.01644691,0.01677336,0.0001817077,0.001159802,0.0260275,0.002270658,0.1985811,0.03254117,0.6747983],"study_design_scores_gemma":[0.01431524,0.01570647,0.03754777,0.006931221,0.01128117,0.00101866,0.0003640662,0.2011164,0.007576559,0.5767792,0.1264419,0.0009214245],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006884723,0.006007789,0.9622627,0.002513438,0.001800366,0.01388537,0.001496227,0.001154741,0.003994653],"genre_scores_gemma":[0.1331389,0.002906245,0.7781893,0.002878523,0.001197358,0.07799359,0.001284906,0.0005985736,0.001812587],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7447946,"threshold_uncertainty_score":0.9184644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9207298509088987,"score_gpt":0.7660337274815276,"score_spread":0.154696123427371,"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."}}