{"id":"W2944654518","doi":"10.1097/jhq.0000000000000245","title":"Reducing Unnecessary Phlebotomy Testing Using a Clinical Decision Support System","year":2020,"lang":"en","type":"article","venue":"Journal for Healthcare Quality","topic":"Clinical Laboratory Practices and Quality Control","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Valacta (Canada)","funders":"","keywords":"Clinical decision support system; Phlebotomy; Medicine; Psychological intervention; Test (biology); Decision support system; Quality management; Activity-based costing; Order entry; Emergency medicine; Medical emergency; Operations management; Computer science; Nursing; Data mining; Surgery","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"],"consensus_categories":[],"category_scores_codex":[0.01531535,0.0002517808,0.001314155,0.00006716402,0.0006168947,0.0001267494,0.000200139,0.0003469039,0.00004186884],"category_scores_gemma":[0.03015669,0.0002063648,0.0005626172,0.0004536354,0.00006537514,0.0003473304,0.00007269951,0.001646531,0.00002100241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003251609,"about_ca_system_score_gemma":0.00195125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002891438,"about_ca_topic_score_gemma":0.00001494437,"domain_scores_codex":[0.9929076,0.001450207,0.003726586,0.0005889768,0.0007603779,0.0005662435],"domain_scores_gemma":[0.984566,0.008791415,0.002244301,0.0004786011,0.001928545,0.001991091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.03554841,0.001329225,0.3552093,0.0139338,0.0008830663,0.001156856,0.00207991,0.0001867136,0.00285361,0.004972263,0.008946447,0.5729005],"study_design_scores_gemma":[0.08247936,0.03554849,0.2082667,0.01768309,0.004569469,0.0080581,0.03755817,0.2391313,0.0004355645,0.003512862,0.3586649,0.004092028],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9235809,0.0006088195,0.02447937,0.04708534,0.002410809,0.001329737,0.0001137957,0.0001856498,0.0002055886],"genre_scores_gemma":[0.9280493,0.00004262944,0.05142809,0.01542572,0.004957757,0.000009848318,0.00001328493,0.00005060284,0.00002280209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5688084,"threshold_uncertainty_score":0.9780127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.513112111832527,"score_gpt":0.5783111412555544,"score_spread":0.06519902942302735,"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."}}