{"id":"W6907862094","doi":"10.25384/sage.21893587","title":"Supplemental Material - Multimorbidity and Complexity Among Patients with Cancer in Ontario: A Retrospective Cohort Study Exploring the Clustering of 17 Chronic Conditions with Cancer","year":2023,"lang":"en","type":"article","venue":"Sage Journals Data","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Retrospective cohort study; Cancer; Multimorbidity; Cohort; Cohort study; Cluster analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"dataset","about_ca_system":false,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["insufficient_payload"],"domain":null,"study_design":"observational","genre":"dataset","about_ca_system":false,"about_ca_topic":true,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005208923,0.0003643953,0.0005047038,0.001884508,0.001499319,0.0008454053,0.00119332,0.0005161955,0.2346394],"category_scores_gemma":[0.01002232,0.000333575,0.0006319275,0.004056501,0.0001560006,0.0003916854,0.000689741,0.0004075508,0.009965004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003875996,"about_ca_system_score_gemma":0.006458923,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5695219,"about_ca_topic_score_gemma":0.7494068,"domain_scores_codex":[0.9994579,0.00004787837,0.0000913554,0.00007156807,0.0002386003,0.00009260501],"domain_scores_gemma":[0.991987,0.001665289,0.001032919,0.0005088025,0.004084437,0.000721448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002470685,0.0002796204,0.2633849,0.000691752,0.0001027898,0.0001467704,0.0004110959,0.0002277387,0.0002774974,0.0004368627,0.7170342,0.01675974],"study_design_scores_gemma":[0.0002707931,0.0001007783,0.9327551,0.0004156922,0.00009977011,0.000274919,0.0005813668,0.000570353,0.0001930585,0.0004006038,0.06429915,0.00003843448],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0154376,0.00006369208,0.0002354948,0.0004029777,0.00004896294,0.0002989446,0.9786647,0.0001282784,0.004719224],"genre_scores_gemma":[0.1408829,0.0004205313,0.004219682,0.0006084132,0.0001390457,0.001921504,0.8288846,0.0001392081,0.02278415],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4304781,"threshold_uncertainty_score":0.8660265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1248056754888545,"score_gpt":0.3624829847869664,"score_spread":0.2376773092981119,"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."}}