{"id":"W2141848375","doi":"10.1002/sim.5951","title":"Multiple‐objective response‐adaptive repeated measurement designs in clinical trials for binary responses","year":2013,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Binary number; Computer science; Basis (linear algebra); Binary data; Function (biology); Mathematical optimization; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1995846,0.0003445785,0.001778779,0.001135464,0.0001075466,0.00006690883,0.0006957885,0.0002311146,0.00063349],"category_scores_gemma":[0.6427038,0.0002397363,0.0001388245,0.001260429,0.0007535795,0.0002173505,0.0001327023,0.000498704,0.0001007809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005192709,"about_ca_system_score_gemma":0.0005265737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005320967,"about_ca_topic_score_gemma":0.0001810071,"domain_scores_codex":[0.9575358,0.03251019,0.005345508,0.00123506,0.002721488,0.0006519348],"domain_scores_gemma":[0.8243493,0.1720648,0.0009832422,0.0007727204,0.001552408,0.0002775141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.1585475,0.001859807,0.04052957,0.00004303313,0.0002817238,0.0006029804,0.01249434,0.0006503083,0.3317276,0.008625367,0.2008538,0.243784],"study_design_scores_gemma":[0.02449125,0.01580341,0.6512699,0.0007233007,0.0001070832,0.0000151836,0.02248097,0.08723338,0.009445006,0.1852901,0.002165764,0.0009746684],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0851509,0.0006621059,0.9049698,0.001488225,0.001558305,0.005174044,0.0002899616,0.00004999827,0.0006566739],"genre_scores_gemma":[0.5450783,0.00003674732,0.4530936,0.0003899835,0.0001661394,0.0005381291,0.000009724306,0.0000330144,0.0006543084],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6107403,"threshold_uncertainty_score":0.9776164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.716823768290405,"score_gpt":0.608469569222505,"score_spread":0.1083541990679,"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."}}