{"id":"W2978005785","doi":"10.1093/labmed/lmz062","title":"The Cost of Pre-Analytical Errors in INR Testing at a Tertiary-Care Hospital Laboratory: Potential for Significant Cost Savings","year":2019,"lang":"en","type":"article","venue":"Laboratory Medicine","topic":"Clinical Laboratory Practices and Quality Control","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Tertiary care; Order entry; Cost analysis; Emergency medicine; Statistics; Operations management; Medical emergency; Reliability engineering; Mathematics; Engineering","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002683621,0.00038961,0.001115703,0.0001510751,0.0001829142,0.00002980231,0.0003403854,0.0002972313,0.0002321199],"category_scores_gemma":[0.01099491,0.0002595074,0.0001453149,0.001125786,0.0005787028,0.0002174991,0.0001249448,0.0006978915,0.00003804124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003048578,"about_ca_system_score_gemma":0.0007813665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007981852,"about_ca_topic_score_gemma":0.0001038881,"domain_scores_codex":[0.9959487,0.0003658191,0.001492296,0.0006921533,0.0008324438,0.0006685663],"domain_scores_gemma":[0.9923861,0.003563888,0.0007678134,0.0009212932,0.001934541,0.0004263499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00550455,0.0009807409,0.9129816,0.001561397,0.0005583591,0.0002145255,0.003428232,0.0001359913,0.05693956,0.002009067,0.01016479,0.005521127],"study_design_scores_gemma":[0.03279008,0.006118248,0.6992763,0.002291851,0.001932323,0.00001237082,0.01618512,0.011261,0.003163939,0.00009261313,0.2257418,0.00113441],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803616,0.001546558,0.00008599237,0.01231592,0.0009081833,0.004034273,0.0003938585,0.00007242044,0.0002811544],"genre_scores_gemma":[0.9958717,0.00006123383,0.0002778213,0.002632436,0.0005762939,0.0002022965,0.0000827205,0.00006992128,0.000225532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.215577,"threshold_uncertainty_score":0.9999857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02852312188292368,"score_gpt":0.3441092410910412,"score_spread":0.3155861192081175,"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."}}