{"id":"W4417292961","doi":"10.1016/j.jpi.2025.100506","title":"Selecting high throughput scanners for clinical usage: a multi-center institution experience","year":2025,"lang":"en","type":"article","venue":"Journal of Pathology Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Throughput; Institution; Quality (philosophy); Digital pathology","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":[0.005240168,0.0001504755,0.0006250649,0.0002613303,0.0006334906,0.00001196838,0.0003161144,0.0004556523,0.00001286993],"category_scores_gemma":[0.003174314,0.0001217144,0.0001558772,0.0002835508,0.0001362642,0.0004735045,0.00008226206,0.001492435,0.0000274257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005328522,"about_ca_system_score_gemma":0.002333174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001720547,"about_ca_topic_score_gemma":0.00006302013,"domain_scores_codex":[0.9948751,0.0006397582,0.003537931,0.0000984827,0.0001894554,0.0006592405],"domain_scores_gemma":[0.9954932,0.001154934,0.002213637,0.0002259418,0.0007755799,0.000136684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001040513,0.00112494,0.7687951,0.005133216,0.0003721887,0.0001198399,0.1051315,0.0002767614,0.0002258408,0.01082652,0.06464295,0.04231054],"study_design_scores_gemma":[0.06633693,0.005582782,0.09987833,0.008504756,0.0004526606,0.002647737,0.1110802,0.03079938,0.0002626158,0.002650885,0.6704712,0.001332516],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.763007,0.0001614182,0.2263132,0.0009702806,0.007246512,0.0009024612,0.000009834917,0.00003246061,0.001356828],"genre_scores_gemma":[0.9327371,0.0004613501,0.05883852,0.006405023,0.0007291628,0.0001049952,0.000006396734,0.00001619927,0.000701306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6689168,"threshold_uncertainty_score":0.6483968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1577451148368999,"score_gpt":0.5534238027801833,"score_spread":0.3956786879432834,"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."}}