{"id":"W1811109694","doi":"10.1111/j.1751-553x.2010.01229.x","title":"Rationale for using insensitive quality control rules for today’s hematology analyzers","year":2010,"lang":"en","type":"article","venue":"International Journal of Laboratory Hematology","topic":"Clinical Laboratory Practices and Quality Control","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta Hospital; Alberta Hospital Edmonton","funders":"","keywords":"Hematology analyzer; Percentile; Hematology; Quality (philosophy); Quality assurance; Medicine; Medical physics; Control (management); Statistics; Instrumentation (computer programming); Computer science; Internal medicine; External quality assessment; Mathematics; Artificial intelligence; Pathology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2391797,0.001989719,0.001910767,0.004899055,0.00370486,0.01075601,0.01124753,0.01105907,0.001433517],"category_scores_gemma":[0.2560108,0.002200733,0.002786213,0.002364624,0.02146324,0.008297572,0.007130352,0.02284194,0.001916245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005026591,"about_ca_system_score_gemma":0.01028843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003782021,"about_ca_topic_score_gemma":0.001757995,"domain_scores_codex":[0.7644588,0.06973721,0.02677613,0.02764018,0.1070343,0.004353503],"domain_scores_gemma":[0.6496901,0.1983011,0.02139168,0.04007459,0.08570238,0.00484017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008082434,0.0009250963,0.01368983,0.001483379,0.0003312319,0.002365478,0.005835007,0.006653907,0.01121945,0.7636058,0.04147045,0.1516122],"study_design_scores_gemma":[0.0008479508,0.002345296,0.01016252,0.006102519,0.0008556598,0.005239326,0.001314226,0.04167812,0.05699274,0.4608499,0.4125877,0.001024023],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01687033,0.006737849,0.8826937,0.05117213,0.005093164,0.002507265,0.0004369323,0.002720828,0.03176776],"genre_scores_gemma":[0.1777138,0.001937735,0.7708185,0.03884506,0.002704525,0.002142953,0.0003301753,0.0004759731,0.00503128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2391797,"threshold_uncertainty_score":0.9382269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06850785969481922,"score_gpt":0.4433778078644802,"score_spread":0.374869948169661,"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."}}