{"id":"W2690746351","doi":"10.1002/cyto.a.23151","title":"Computer‐aided diagnostics in digital pathology","year":2017,"lang":"en","type":"editorial","venue":"Cytometry Part A","topic":"AI in cancer detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Centre for Applied Research in Cancer Control; Occupational Cancer Research Centre","funders":"","keywords":"Digital pathology; Computer science; Pathology; Medical physics; Medicine; Computer graphics (images)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004674189,0.000403133,0.0006599882,0.0006699445,0.0001599846,0.000929374,0.002709459,0.001051545,0.000009285113],"category_scores_gemma":[0.002447095,0.000436436,0.0001436934,0.0005602767,0.0001644976,0.0008598423,0.001238729,0.001217661,0.0003690347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003941866,"about_ca_system_score_gemma":0.0004614058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001451613,"about_ca_topic_score_gemma":0.00002355635,"domain_scores_codex":[0.9970067,0.00006649278,0.0005026286,0.001061192,0.0007807392,0.0005822534],"domain_scores_gemma":[0.9955595,0.001687665,0.0004788754,0.001906656,0.0002284286,0.0001388935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007189594,0.00005067609,0.000486884,0.00004474346,0.00001706612,0.0004811924,0.00007258513,0.00001587167,0.000001840977,0.00004701387,0.9598917,0.03888321],"study_design_scores_gemma":[0.0005482063,0.0002404573,0.000532944,0.0002393896,0.000009441218,0.00002437395,0.00000172088,0.001525043,0.0000358587,0.000966421,0.9953882,0.0004879845],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0002596547,0.000464611,0.1284023,0.00006678213,0.8692779,0.0002130522,0.0001104906,0.0002265876,0.0009785757],"genre_scores_gemma":[0.004751873,0.0007328234,0.004372158,0.00005931506,0.9891027,0.00009423835,0.0001475954,0.00006070857,0.0006785375],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1240302,"threshold_uncertainty_score":0.9998087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01594668213661012,"score_gpt":0.2836435746872094,"score_spread":0.2676968925505993,"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."}}