{"id":"W2953440485","doi":"10.1136/jclinpath-2019-205874","title":"Developing a pan-cancer research autopsy programme","year":2019,"lang":"en","type":"article","venue":"Journal of Clinical Pathology","topic":"Autopsy Techniques and Outcomes","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"National Cancer Institute; Princess Margaret Cancer Foundation","keywords":"Autopsy; Medicine; Cancer; Pathology; Bioinformatics; Data science; Biology; Internal medicine; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006677972,0.0001002073,0.0008785585,0.0001881072,0.00003412449,0.00001212993,0.0002085382,0.0003292704,0.0002510227],"category_scores_gemma":[0.002278037,0.00006579821,0.0003509218,0.0002293866,0.0001921449,0.00006708824,0.0001024127,0.001527478,0.0001028553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008229166,"about_ca_system_score_gemma":0.0008045717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005566146,"about_ca_topic_score_gemma":0.000002615643,"domain_scores_codex":[0.997198,0.0003833384,0.001377426,0.0001968363,0.0004395249,0.0004048547],"domain_scores_gemma":[0.997519,0.0008790093,0.0005016873,0.0002610233,0.0006739327,0.0001653805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006604023,0.0002896102,0.756331,0.0000953612,0.00009768289,0.001441796,0.0001273404,3.327679e-7,0.001360546,0.004874251,0.002740477,0.2319812],"study_design_scores_gemma":[0.002705523,0.00305158,0.849902,0.0003514072,0.00006103324,0.001626436,0.0001284253,0.0000212539,0.0004967554,0.005738912,0.135802,0.0001146478],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790265,0.0004729583,0.001019088,0.01599216,0.001007566,0.0003058859,6.914398e-7,0.00003220756,0.002142929],"genre_scores_gemma":[0.9527675,0.0009237614,0.04003192,0.002654831,0.0009286038,0.000008094617,7.555179e-7,0.00002280899,0.002661762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2318665,"threshold_uncertainty_score":0.6636217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3402582232272668,"score_gpt":0.5793447481990514,"score_spread":0.2390865249717845,"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."}}