{"id":"W2995663464","doi":"10.1111/his.13991","title":"The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies","year":2019,"lang":"en","type":"review","venue":"Histopathology","topic":"AI in cancer detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency; Vancouver General Hospital","funders":"","keywords":"Cornerstone; Modalities; Pathology; Morphology (biology); Medicine; Surgical pathology; Anatomical pathology; Biology; History; Zoology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.001829418,0.0007078757,0.001172954,0.003544541,0.0004738941,0.001986061,0.0009920208,0.002267519,0.006539132],"category_scores_gemma":[0.003212869,0.0003251239,0.0007041693,0.005576241,0.00173607,0.004329022,0.001060733,0.002487147,0.002394444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278385,"about_ca_system_score_gemma":0.001975816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001967431,"about_ca_topic_score_gemma":0.003258179,"domain_scores_codex":[0.9994105,0.0001493476,0.00009094078,0.00009178489,0.000203967,0.0000535494],"domain_scores_gemma":[0.9961325,0.00258,0.0003150949,0.00009842595,0.0006954824,0.0001785133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004395362,0.00003140787,0.0003691434,0.01881137,0.00006563953,0.000193186,0.0002131761,0.0003733419,0.0008339871,0.01065804,0.04555426,0.9228523],"study_design_scores_gemma":[0.000005670044,0.00005093971,0.001070912,0.005628262,0.00006317503,0.001328033,0.0002518312,0.00009985171,0.0002220927,0.005295696,0.9859585,0.00002508016],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00009980347,0.9968041,0.000210045,0.001346516,0.0004833495,0.000002818206,0.00001711505,0.00001044912,0.001025697],"genre_scores_gemma":[0.0007815736,0.9966938,0.0003920263,0.0009691101,0.0006116352,0.000004320036,0.0000278164,0.000003506077,0.00051624],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006539132,"threshold_uncertainty_score":0.02187556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.107511134361819,"score_gpt":0.3203510805771286,"score_spread":0.2128399462153096,"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."}}