{"id":"W2049087175","doi":"10.1371/journal.pone.0113432","title":"Clinical Management and Burden of Prostate Cancer: A Markov Monte Carlo Model","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"McGill University","keywords":"Life expectancy; Medicine; Prostate cancer; Markov model; Cohort; Statistics; Cancer; Markov chain; Computer science; Internal medicine; Machine learning; Population; Mathematics; Environmental health","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":[0.002859877,0.0009807191,0.001613298,0.001099632,0.000984353,0.001978564,0.002225995,0.002694888,0.005783374],"category_scores_gemma":[0.009371012,0.0009515071,0.001506533,0.001121102,0.001578194,0.001134006,0.001087554,0.002200224,0.0005302001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003185775,"about_ca_system_score_gemma":0.003356881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06767888,"about_ca_topic_score_gemma":0.0300975,"domain_scores_codex":[0.9987705,0.0006431189,0.00004428328,0.0002000102,0.0001101645,0.0002318449],"domain_scores_gemma":[0.9908984,0.007447647,0.0006624826,0.0001605141,0.0005119696,0.0003190314],"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":[0.00007082117,0.00003145,0.002473335,0.00001929999,0.00003195279,0.00007852237,0.00004332614,0.9883183,0.00006085165,0.007239556,0.0004653298,0.001167388],"study_design_scores_gemma":[0.00004126905,0.00002156652,0.0003788762,0.00001356867,0.00002260691,0.00002050377,0.00001935154,0.99532,0.00002470624,0.003873863,0.000253205,0.00001043523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5121061,0.002205955,0.4430697,0.008253563,0.0003764313,0.0008269013,0.005425039,0.0006246208,0.02711164],"genre_scores_gemma":[0.9630779,0.0008960578,0.02412595,0.0005035725,0.0001227735,0.0007767131,0.00132712,0.00005152978,0.009118398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06767888,"threshold_uncertainty_score":0.1345699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04880403249984613,"score_gpt":0.2981868082964446,"score_spread":0.2493827757965984,"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."}}