{"id":"W2908040054","doi":"10.1093/ajcp/142.suppl1.196","title":"Pathology Informatics Trends in Anatomical Pathology: Analysis of 11 Years of United States and Canadian Academy of Pathology Abstracts","year":2014,"lang":"en","type":"article","venue":"American Journal of Clinical Pathology","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pathology; Anatomical pathology; Medicine; Bone pathology; Surgical pathology; Molecular pathology; Renal pathology; Clinical pathology; Biology; Internal medicine; Immunohistochemistry; Kidney","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01934017,0.0009831809,0.001305871,0.1187121,0.003101154,0.007037397,0.002309344,0.0009956221,0.006950572],"category_scores_gemma":[0.08670084,0.0007435643,0.001778133,0.09531067,0.001642143,0.004055678,0.004181483,0.001015078,0.002515434],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01773272,"about_ca_system_score_gemma":0.03965367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2171954,"about_ca_topic_score_gemma":0.293326,"domain_scores_codex":[0.9734081,0.002274854,0.006420435,0.001841565,0.01477673,0.001278347],"domain_scores_gemma":[0.8131921,0.02299679,0.03512594,0.002276669,0.1167612,0.009647252],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000803809,0.0001063075,0.2841662,0.045086,0.0009053526,0.00217081,0.01871393,0.0005045324,0.003322941,0.00295202,0.2235193,0.4177489],"study_design_scores_gemma":[0.00002928369,0.0001286017,0.5282216,0.01402764,0.000592321,0.003518788,0.01351056,0.0003725496,0.000972532,0.0004206629,0.4379963,0.0002091609],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1808972,0.6189516,0.002774605,0.01508253,0.003424844,0.001454329,0.1122941,0.001147336,0.06397368],"genre_scores_gemma":[0.4314663,0.4676043,0.01047269,0.004565436,0.00200032,0.001272414,0.06309091,0.0006738238,0.01885391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9822673,"threshold_uncertainty_score":0.4318624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02867946894839402,"score_gpt":0.3568480397958585,"score_spread":0.3281685708474645,"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."}}