{"id":"W3159699626","doi":"10.1136/jclinpath-2021-207524","title":"Evaluation of an open-source machine-learning tool to quantify bone marrow plasma cells","year":2021,"lang":"en","type":"article","venue":"Journal of Clinical Pathology","topic":"Hematological disorders and diagnostics","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; London Health Sciences Centre","funders":"","keywords":"Concordance; Kappa; Bone marrow; Intraclass correlation; Medicine; Plasma cell; Digital image analysis; Classifier (UML); Pathology; Digital pathology; Nuclear medicine; Reproducibility; Artificial intelligence; Computer science; Internal medicine; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0154348,0.001603593,0.0007018339,0.002954622,0.000651092,0.00191907,0.004396379,0.001951262,0.00299186],"category_scores_gemma":[0.02573061,0.0004508585,0.000803922,0.0009405579,0.0007616821,0.002159583,0.002475898,0.0009372392,0.001966272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001422823,"about_ca_system_score_gemma":0.001685192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003139911,"about_ca_topic_score_gemma":0.002880691,"domain_scores_codex":[0.9898884,0.00269964,0.0008083406,0.001786019,0.004406864,0.0004107917],"domain_scores_gemma":[0.9714579,0.01116606,0.001521898,0.001936374,0.01279769,0.001120012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00366656,0.002803933,0.03881868,0.001308616,0.0005233714,0.001101756,0.0008676185,0.0301073,0.1030542,0.001745758,0.02088117,0.7951211],"study_design_scores_gemma":[0.0009464465,0.003447297,0.07550439,0.0003998497,0.0002506572,0.002308098,0.0003483142,0.7341077,0.1504222,0.002028472,0.02995134,0.0002852346],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4990034,0.001800104,0.4379821,0.0006453086,0.000577635,0.002599186,0.002563403,0.04951091,0.005318047],"genre_scores_gemma":[0.5147977,0.0002933143,0.4673036,0.0005334144,0.0001260721,0.001368821,0.008309059,0.002442365,0.004825731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0154348,"threshold_uncertainty_score":0.08162802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1524654231998943,"score_gpt":0.4575364076332669,"score_spread":0.3050709844333725,"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."}}