{"id":"W2299201707","doi":"10.1097/pgp.0000000000000274","title":"An Immunohistochemical Algorithm for Ovarian Carcinoma Typing","year":2016,"lang":"en","type":"article","venue":"International Journal of Gynecological Pathology","topic":"Ovarian cancer diagnosis and treatment","field":"Medicine","cited_by":230,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; University of Calgary; Université de Montréal; University of Alberta; University of British Columbia; Alberta Health Services; University of Ottawa; Calgary Laboratory Services; Foothills Medical Centre","funders":"Canadian Institutes of Health Research; Cure Brain Cancer Foundation; University Health Network; Terry Fox Research Institute; Ontario Institute for Cancer Research; BC Cancer Agency; Calgary Laboratory Services","keywords":"Immunohistochemistry; Pathology; Typing; Carcinoma; Ovarian carcinoma; Medicine; Biology; Internal medicine; Ovarian cancer; Cancer; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.003421197,0.001237948,0.001011394,0.002661728,0.001131031,0.002868349,0.002311901,0.001027211,0.007733671],"category_scores_gemma":[0.008727895,0.0007182929,0.001441911,0.001466626,0.0004751373,0.001095934,0.001692732,0.001584972,0.005445335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001491345,"about_ca_system_score_gemma":0.003054803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007026416,"about_ca_topic_score_gemma":0.008148915,"domain_scores_codex":[0.998256,0.0003235302,0.0002222815,0.0004760836,0.0005772565,0.0001447393],"domain_scores_gemma":[0.9975018,0.0006278781,0.0001937068,0.0003334459,0.001263385,0.00007975091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004330982,0.0001903072,0.02836519,0.0004053923,0.000242315,0.0004706189,0.000267315,0.06466595,0.01144964,0.01143723,0.0403656,0.8417075],"study_design_scores_gemma":[0.0001602856,0.0001347807,0.007630227,0.0001276556,0.0001420332,0.0009706737,0.0001463427,0.9040814,0.008405734,0.02621868,0.05191768,0.00006449923],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006031541,0.0002527945,0.9832691,0.0003727801,0.0001224749,0.0007997584,0.0009888798,0.005206341,0.002956429],"genre_scores_gemma":[0.04412272,0.0001945454,0.9486288,0.0001682473,0.00005635016,0.0009972671,0.002434744,0.0003184355,0.003078901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007733671,"threshold_uncertainty_score":0.02587169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193803236188791,"score_gpt":0.3277084716529505,"score_spread":0.3057704392910626,"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."}}