{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006817342,0.000288577,0.0009030736,0.0003711189,0.0001488471,0.0001078256,0.001248582,0.0007101378,0.000006846226],"category_scores_gemma":[0.000454163,0.0002217861,0.0001203475,0.0005918276,0.0004964534,0.0001477022,0.0007523914,0.000803005,0.0001406486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004221147,"about_ca_system_score_gemma":0.0003567266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001104941,"about_ca_topic_score_gemma":0.0001144653,"domain_scores_codex":[0.9976186,0.0003405059,0.0006928703,0.0007929979,0.0001395967,0.0004154615],"domain_scores_gemma":[0.9979728,0.0007695379,0.0003850481,0.0008043675,0.00003054395,0.00003772066],"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":[0.000001383113,0.000009252234,0.00001759616,0.0001487071,0.000001563378,0.0001600221,0.0001069698,0.000002520969,4.037223e-7,0.02338904,0.0002210576,0.9759415],"study_design_scores_gemma":[0.00003757111,0.0001521446,0.000459552,0.000208553,0.00003158369,0.0007389542,0.00003995009,0.0001316933,0.000001035839,0.03725806,0.9606608,0.0002801033],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002119789,0.945611,0.05052166,0.0003889544,0.002292961,0.0005417843,0.000002699293,0.0002855971,0.0003341649],"genre_scores_gemma":[0.0003183015,0.9955391,0.003640668,0.00005247022,0.00007906558,0.0001253966,0.000003903649,0.00002016729,0.0002209527],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9756614,"threshold_uncertainty_score":0.9044176,"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."}}