{"id":"W1976347544","doi":"10.4103/2153-3539.151922","title":"Performance of the CellaVision® DM96 system for detecting red blood cell morphologic abnormalities","year":2015,"lang":"en","type":"article","venue":"Journal of Pathology Informatics","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services; University of Calgary","funders":"","keywords":"Computer science; Pathology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002846337,0.0004278377,0.0003221369,0.001093261,0.0002184936,0.0008806456,0.0008497669,0.0004910046,0.002449916],"category_scores_gemma":[0.005057644,0.0002234213,0.000276699,0.0004832291,0.0002526885,0.0003479631,0.0006594342,0.0003264339,0.0008558872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005724934,"about_ca_system_score_gemma":0.0005495672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002184522,"about_ca_topic_score_gemma":0.001642446,"domain_scores_codex":[0.9974739,0.0005383518,0.0002179807,0.0007076957,0.0009550102,0.0001071602],"domain_scores_gemma":[0.9977301,0.000836946,0.0002833278,0.000192824,0.000855883,0.0001009377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005875176,0.0003986706,0.3441536,0.0006731283,0.0002799094,0.0003774219,0.000691521,0.003936983,0.3648829,0.00103316,0.00501925,0.2726783],"study_design_scores_gemma":[0.0002719282,0.002938654,0.2831315,0.0001810236,0.0003057231,0.003686609,0.0003613688,0.1009878,0.5910701,0.0005932315,0.01630305,0.0001689953],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9554186,0.001365968,0.035939,0.0001431812,0.00008975103,0.0003521222,0.001202993,0.001129803,0.00435847],"genre_scores_gemma":[0.9206223,0.0003324311,0.07464237,0.0001556362,0.0000302616,0.0002428534,0.001207101,0.00009921166,0.002667772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002846337,"threshold_uncertainty_score":0.01505303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02029329069876548,"score_gpt":0.2287078453914094,"score_spread":0.2084145546926439,"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."}}