{"id":"W2917346689","doi":"10.1136/jclinpath-2019-205725","title":"The adaptation of AABACUS for quality improvement in laboratory workflow analysis (\"L-AABACUS\")","year":2019,"lang":"en","type":"article","venue":"Journal of Clinical Pathology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Workflow; Workload; Computer science; Turnaround time; Staffing; Adaptation (eye); Quality management; Key (lock); Medicine; Operations management; Engineering; Database; Operating system; Management system","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":[0.004627796,0.00008147878,0.0005114871,0.00006500522,0.00002329555,0.000005785034,0.0002353308,0.0002803609,0.000005306813],"category_scores_gemma":[0.002803535,0.00005082351,0.0003912173,0.0001777477,0.0001991281,0.000002354305,0.00004853127,0.0002108267,0.000001173121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007953156,"about_ca_system_score_gemma":0.0001597338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005374526,"about_ca_topic_score_gemma":0.0001382407,"domain_scores_codex":[0.9978029,0.0003834096,0.00135759,0.0001698649,0.0001248902,0.0001614107],"domain_scores_gemma":[0.9976898,0.0007609589,0.0009548448,0.0002424785,0.0002952382,0.0000566711],"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":[0.002654112,0.0005304389,0.3204994,0.00005174621,0.0009228176,0.00001599513,0.0001486224,0.0005985621,0.07967533,0.000216662,0.0006051651,0.5940812],"study_design_scores_gemma":[0.008769791,0.01202255,0.8836784,0.00006172078,0.0006059359,0.00001566826,0.002180953,0.000990798,0.01296499,0.002286461,0.07602668,0.0003960915],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899997,0.001012896,0.007700387,0.0005679874,0.0005540947,0.0001093915,0.00001587097,0.000001569751,0.00003813952],"genre_scores_gemma":[0.9931763,0.0004246631,0.005882803,0.0002130194,0.0001915645,0.00000471473,0.000007960409,0.000005478712,0.00009343389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5936851,"threshold_uncertainty_score":0.3356296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0535430150716385,"score_gpt":0.414244089975711,"score_spread":0.3607010749040725,"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."}}