{"id":"W4409368810","doi":"10.1002/sim.70049","title":"A Generalized Phase I/II Dose Optimization Trial Design With Multi‐Categorical and Multi‐Graded Outcomes","year":2025,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Simon Fraser University","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Categorical variable; Monotonic function; Benchmarking; Robustness (evolution); Computer science; Maximum tolerated dose; Optimal design; Clinical trial; Continuous variable; Reliability (semiconductor); Reliability engineering; Mathematical optimization; Medicine; Mathematics; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.02782533,0.001654563,0.002802215,0.0007516405,0.000424378,0.001132257,0.001791637,0.001725878,0.004405577],"category_scores_gemma":[0.02937171,0.0007211987,0.00215466,0.0008361079,0.001881585,0.0009944419,0.001466186,0.002683118,0.0005793574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007761798,"about_ca_system_score_gemma":0.00258822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000347725,"about_ca_topic_score_gemma":0.0004555795,"domain_scores_codex":[0.9719297,0.02387056,0.0007843833,0.001825578,0.001179792,0.0004099431],"domain_scores_gemma":[0.9881675,0.007073447,0.001605154,0.001969367,0.0007430228,0.000441513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.02694007,0.00260703,0.01133871,0.003752013,0.002866404,0.0005764166,0.0006008149,0.494186,0.02144025,0.1316745,0.006519361,0.2974983],"study_design_scores_gemma":[0.01849744,0.02529211,0.00670362,0.0004035447,0.001898604,0.0005777091,0.0001191951,0.7919222,0.009773413,0.1309878,0.01354928,0.0002751902],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04211964,0.0007450781,0.9418533,0.000747292,0.0002185897,0.0118706,0.0004598631,0.0003987966,0.001586805],"genre_scores_gemma":[0.3633072,0.0004402484,0.599354,0.001474008,0.0001148125,0.03260459,0.000414608,0.00008112189,0.002209417],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02782533,"threshold_uncertainty_score":0.1471562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5347114705751628,"score_gpt":0.5995985752183284,"score_spread":0.0648871046431656,"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."}}