{"id":"W4385619915","doi":"10.1007/s00428-023-03611-9","title":"Redefining and capturing pre-analytic deficiencies in an anatomical pathology laboratory: a quality improvement initiative","year":2023,"lang":"en","type":"article","venue":"Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin","topic":"Clinical Laboratory Practices and Quality Control","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Sunnybrook Hospital; Sunnybrook Health Science Centre","funders":"","keywords":"Requisition; Quality assurance; Documentation; Medicine; Quality (philosophy); Quality management; Medical diagnosis; Medical physics; Operations management; Data collection; Pathology; Computer science; Business; Engineering; External quality assessment; Statistics; 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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006710614,0.0006740158,0.001870702,0.0006193692,0.0003093238,0.00008361619,0.0004976477,0.0005553557,0.00004214195],"category_scores_gemma":[0.01058798,0.0005268165,0.0002442426,0.001448211,0.001580859,0.0004161119,0.0006004212,0.002030567,0.00006000618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001354201,"about_ca_system_score_gemma":0.0006950264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000288432,"about_ca_topic_score_gemma":0.0003037022,"domain_scores_codex":[0.9922335,0.002211385,0.001888733,0.001703563,0.0006797525,0.001283102],"domain_scores_gemma":[0.9920053,0.005115562,0.0007482866,0.001242305,0.0002750415,0.0006134701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007249864,0.003831167,0.5699911,0.001092396,0.001248265,0.005470239,0.02032931,0.000520505,0.3099406,0.0325769,0.0005221275,0.04722755],"study_design_scores_gemma":[0.00987652,0.004429722,0.9469349,0.0003193485,0.0008169189,0.00002875458,0.01313556,0.009554028,0.002342346,0.00930886,0.002115535,0.001137498],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918548,0.001298316,0.0001712258,0.003849509,0.0002662994,0.0009731817,0.0001962225,0.0004097391,0.0009806913],"genre_scores_gemma":[0.9910455,0.001085444,0.001505218,0.005597461,0.0002627866,0.0001296133,0.0002635151,0.00006411134,0.00004636969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3769438,"threshold_uncertainty_score":0.9997184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1120472801416199,"score_gpt":0.4516517251689904,"score_spread":0.3396044450273706,"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."}}