{"id":"W7105997481","doi":"10.64483/202412249","title":"The Impact of Automation on Clinical Laboratory Efficiency and Error Reduction","year":2024,"lang":"","type":"article","venue":"Saudi Journal of Medicine and Public Health","topic":"Clinical Laboratory Practices and Quality Control","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Automation; Workflow; Turnaround time; Standardization; Laboratory automation; Adaptation (eye); Human error; Control (management)","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":[],"consensus_categories":[],"category_scores_codex":[0.009988544,0.0004144706,0.0003051371,0.001485058,0.0005445105,0.003602757,0.0005702478,0.0007694323,0.002526058],"category_scores_gemma":[0.04479625,0.0001637127,0.0005501558,0.001921403,0.001045658,0.002021612,0.001461724,0.0005975569,0.0007273566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00167612,"about_ca_system_score_gemma":0.003218258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001739577,"about_ca_topic_score_gemma":0.001359956,"domain_scores_codex":[0.973686,0.01409745,0.001164017,0.001069097,0.008596395,0.001387034],"domain_scores_gemma":[0.9131311,0.06262555,0.008438347,0.00363092,0.01089953,0.001274505],"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":[0.001143307,0.0005403599,0.1989888,0.001540521,0.0003216933,0.0005557243,0.003093404,0.0209662,0.005110585,0.02184633,0.004546872,0.7413462],"study_design_scores_gemma":[0.000117947,0.004951059,0.8414634,0.001813747,0.0006211123,0.002225463,0.005589865,0.03175363,0.01911344,0.02557624,0.06659118,0.0001829621],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8177493,0.02357197,0.04132133,0.01075992,0.0003508213,0.0002616298,0.0004561508,0.0003530002,0.105176],"genre_scores_gemma":[0.9891985,0.001901028,0.007287894,0.000292876,0.0001566112,0.00002854575,0.0000583123,0.0000209627,0.001055284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009988544,"threshold_uncertainty_score":0.05282509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1724975323390227,"score_gpt":0.5241841170222559,"score_spread":0.3516865846832332,"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."}}