{"id":"W4387327086","doi":"","title":"Current status of discrete data capture in synoptic surgical pathology and cancer reporting","year":2015,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Current (fluid); Surgical pathology; Data science; Medicine; Pathology; Computer science; Geology; Oceanography","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"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3362592,0.0006507101,0.001070863,0.01232346,0.002163783,0.01978347,0.009100577,0.003230279,0.004970516],"category_scores_gemma":[0.3668355,0.001699194,0.001664408,0.01562078,0.01024485,0.02232129,0.01334911,0.004774323,0.001136703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01184313,"about_ca_system_score_gemma":0.01798331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0169449,"about_ca_topic_score_gemma":0.007589194,"domain_scores_codex":[0.7421412,0.1551196,0.02861156,0.01055693,0.05976214,0.00380848],"domain_scores_gemma":[0.3334556,0.4384401,0.04990685,0.06231316,0.1086342,0.007250068],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004167869,0.0001747301,0.0581935,0.006339545,0.0001702905,0.000126554,0.009873275,0.002587908,0.0009141882,0.07231431,0.01980709,0.8290818],"study_design_scores_gemma":[0.0002244327,0.001728186,0.07968444,0.03659939,0.0003402635,0.002762969,0.04906037,0.03043964,0.0097275,0.1091175,0.679446,0.0008692852],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08613329,0.1426681,0.4004506,0.3148426,0.003783494,0.001917482,0.002461709,0.003294767,0.0444481],"genre_scores_gemma":[0.4712758,0.06577516,0.4339581,0.01603426,0.003392573,0.001960587,0.002940274,0.000698923,0.003964346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6637408,"threshold_uncertainty_score":0.8185106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.42317847237394,"score_gpt":0.5872531607031526,"score_spread":0.1640746883292126,"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."}}