{"id":"W3028357212","doi":"10.1002/sim.8868","title":"The optimal design of clinical trials with potential biomarker effects: A novel computational approach","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Western Canada Research Grid; Compute Canada","keywords":"Computer science; Scalability; Smoothing; Curse of dimensionality; Personalized medicine; Clinical trial; Software; Data mining; Machine learning; Bioinformatics","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":[0.009266485,0.001909621,0.003983685,0.002923657,0.0007985735,0.002595398,0.002574145,0.002309495,0.006069054],"category_scores_gemma":[0.02870809,0.002024596,0.003287137,0.002793238,0.002256897,0.001861228,0.002756765,0.003414721,0.0005777348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001924378,"about_ca_system_score_gemma":0.007739719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006113294,"about_ca_topic_score_gemma":0.00662145,"domain_scores_codex":[0.995188,0.003182682,0.0002187196,0.0004931844,0.0006769894,0.0002403631],"domain_scores_gemma":[0.9672125,0.0294883,0.001033549,0.0007421088,0.0009444384,0.0005791241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001463509,0.00006474672,0.0007011702,0.0002272268,0.0002098708,0.00007835445,0.00005740789,0.9434711,0.0002588904,0.02777366,0.001625882,0.02538529],"study_design_scores_gemma":[0.0001051578,0.00003648967,0.00007666231,0.00003103472,0.00005866988,0.00001988182,0.000009030301,0.9626015,0.0001089053,0.03563892,0.001303837,0.000009954641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003781161,0.0006273508,0.9926668,0.0008650966,0.00008790555,0.0001678112,0.0001300703,0.0002189611,0.001454987],"genre_scores_gemma":[0.1018677,0.0007936087,0.8920577,0.0008357665,0.0002632521,0.001518138,0.0004595561,0.0002442468,0.001960007],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009266485,"threshold_uncertainty_score":0.0490064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6846230401092185,"score_gpt":0.6258049677645551,"score_spread":0.05881807234466341,"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."}}