{"id":"W4417292933","doi":"10.1016/j.jpi.2025.100507","title":"Digital pathology implementation in a multi-site hospital network: the devil is in the details","year":2025,"lang":"en","type":"article","venue":"Journal of Pathology Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Digital pathology; MEDLINE; Digital health; Quality (philosophy)","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":[0.001766051,0.0001115101,0.0002015465,0.0002346607,0.00009212879,0.0001555935,0.0008650168,0.00009673413,0.000003145189],"category_scores_gemma":[0.00009400363,0.0000677831,0.00007809571,0.0006804317,0.00008578908,0.00112388,0.0001875817,0.0005193356,0.000009166566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001244351,"about_ca_system_score_gemma":0.0001517442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000306082,"about_ca_topic_score_gemma":0.0001048262,"domain_scores_codex":[0.998289,0.0001682941,0.0009851594,0.00007231681,0.0002025097,0.0002827295],"domain_scores_gemma":[0.9987016,0.0002441934,0.0006191202,0.0003016754,0.0001142801,0.00001909862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004951663,0.0002448531,0.3381068,0.00006725813,0.00006652695,0.0008069592,0.1487847,0.006305004,0.00005419974,0.004956762,0.008912628,0.4916448],"study_design_scores_gemma":[0.006937987,0.00174981,0.804117,0.0002881474,0.00008024908,0.00593636,0.03087324,0.1130992,0.0003406083,0.02039444,0.01560209,0.0005808311],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5412202,0.0002248485,0.4526459,0.004288455,0.0009373896,0.0002575701,0.000003552688,0.000009876819,0.0004121008],"genre_scores_gemma":[0.9812687,0.000111973,0.01472391,0.003804118,0.00005889003,0.00001502591,0.000001197264,0.000003376925,0.00001277199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4910639,"threshold_uncertainty_score":0.2764115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01417410539031838,"score_gpt":0.3007397860105377,"score_spread":0.2865656806202193,"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."}}