{"id":"W2046793647","doi":"10.1016/j.ymeth.2014.03.018","title":"Model predictive control for optimally scheduling intermittent androgen suppression of prostate cancer","year":2014,"lang":"en","type":"article","venue":"Methods","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Health Canada","keywords":"Androgen suppression; Model predictive control; Prostate cancer; Computer science; Schedule; Androgen; Internal medicine; Medicine; Oncology; Hormone; Control theory (sociology); Control (management); Cancer; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001466019,0.001230254,0.001597498,0.0004942259,0.000529693,0.001425176,0.0009774528,0.0008376361,0.001888974],"category_scores_gemma":[0.003461133,0.0006981795,0.0005743062,0.0005873974,0.0006220542,0.0006276721,0.0008771744,0.001800496,0.0002385515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144669,"about_ca_system_score_gemma":0.002092769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02437112,"about_ca_topic_score_gemma":0.01626974,"domain_scores_codex":[0.999446,0.0001834658,0.0000250438,0.0001061687,0.0001290914,0.0001103562],"domain_scores_gemma":[0.9985411,0.001050382,0.0001564543,0.0000387601,0.000168242,0.00004513749],"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":[0.00009556891,0.00003734841,0.0001829978,0.00004340867,0.00002686001,0.0000108946,0.00001935523,0.9850327,0.0003397586,0.001434609,0.0004449106,0.01233171],"study_design_scores_gemma":[0.00001222928,0.00001761002,0.00007689276,0.000002901036,0.000004828307,0.000001387461,0.000002522308,0.9990867,0.000112397,0.0005851874,0.00009478458,0.000002437923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04724088,0.001282269,0.944452,0.000819705,0.0002375808,0.00009771921,0.0002301099,0.0007331552,0.004906408],"genre_scores_gemma":[0.9717717,0.0003185417,0.02486267,0.0001272172,0.00009004824,0.0001605516,0.0001662712,0.00004773017,0.002455335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02437112,"threshold_uncertainty_score":0.04845852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06005106731247348,"score_gpt":0.4355313209340246,"score_spread":0.3754802536215511,"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."}}