{"id":"W2048726408","doi":"10.3410/m4-16","title":"Active surveillance for low-risk prostate cancer","year":2012,"lang":"en","type":"article","venue":"F1000 Medicine Reports","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Prostate cancer; Cancer; Data science; Computer science; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0006337402,0.0002613642,0.000614854,0.00008715426,0.0001075838,0.000005657564,0.0000353714,0.0000810604,0.0003684721],"category_scores_gemma":[0.0005525032,0.0001760689,0.0001209591,0.000192987,0.000121259,0.0001233416,0.00002442655,0.0001580889,0.000009451351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003502675,"about_ca_system_score_gemma":0.0002178887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007561184,"about_ca_topic_score_gemma":0.0000489796,"domain_scores_codex":[0.9980089,0.00003361443,0.0005781099,0.0004015691,0.0003820537,0.0005957386],"domain_scores_gemma":[0.9981123,0.0002105642,0.0004935145,0.0004697334,0.0002741305,0.0004397782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003668198,0.0003145579,0.8892034,0.0002386168,0.0003132805,0.0001984645,0.001459585,0.000005947983,0.000463998,0.00002400765,0.03756113,0.06985018],"study_design_scores_gemma":[0.00330585,0.0009141374,0.6571102,0.0007440809,0.0004838626,0.0004608269,0.0002759369,0.00002261718,0.01334181,0.000185055,0.3228724,0.0002832542],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9640962,0.01973858,0.0001020768,0.006462459,0.002478937,0.002944688,0.000136291,0.0001516422,0.003889071],"genre_scores_gemma":[0.9879335,0.005636652,0.0002189117,0.0007230766,0.001805829,0.001497624,0.0001904091,0.00005593429,0.001938102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2853113,"threshold_uncertainty_score":0.7179883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01678113400065694,"score_gpt":0.3207943245553357,"score_spread":0.3040131905546787,"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."}}