{"id":"W4200216442","doi":"10.3390/curroncol28060432","title":"Examining the Landscape of Prognostic Factors and Clinical Outcomes for Cancer Control","year":2021,"lang":"en","type":"article","venue":"Current Oncology","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"CRDF Global","keywords":"Medicine; Data collection; Conceptualization; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.05768793,0.0002636455,0.000566061,0.002477146,0.003939703,0.01000404,0.001332648,0.000827609,0.002353753],"category_scores_gemma":[0.07008588,0.0002906013,0.0003988873,0.003552565,0.01117551,0.007866801,0.006744616,0.002631978,0.0001065296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01183726,"about_ca_system_score_gemma":0.01679537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0114178,"about_ca_topic_score_gemma":0.01057247,"domain_scores_codex":[0.9589459,0.03426019,0.001088075,0.001591915,0.002203418,0.001910429],"domain_scores_gemma":[0.9425761,0.04292034,0.005142578,0.002031279,0.00444451,0.002885163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002790253,0.0001385586,0.1220438,0.001438099,0.00009649029,0.0009998324,0.4836445,0.001602787,0.0009738011,0.2347178,0.006075447,0.1479899],"study_design_scores_gemma":[0.00006745929,0.000368702,0.07447456,0.003976426,0.0001092592,0.0006322167,0.6649672,0.002852323,0.0008450738,0.1375869,0.1139579,0.0001621054],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6322491,0.0145774,0.03138692,0.2460198,0.0004644954,0.0005880716,0.0005517906,0.00008380044,0.07407875],"genre_scores_gemma":[0.994175,0.00147108,0.002536145,0.001182696,0.0000480405,0.000115567,0.00006919535,0.00001303546,0.0003892842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05768793,"threshold_uncertainty_score":0.3050866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3753791192985335,"score_gpt":0.5098409758292985,"score_spread":0.1344618565307651,"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."}}