{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04169779,0.0002823111,0.001337973,0.0004094604,0.0003389501,0.0001202823,0.0001544494,0.0002620758,0.0001607176],"category_scores_gemma":[0.01747108,0.0001438698,0.0002633251,0.0009440364,0.001193697,0.0004788623,0.00003621571,0.001936892,0.000004541577],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002081417,"about_ca_system_score_gemma":0.00691979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001806734,"about_ca_topic_score_gemma":0.000009952646,"domain_scores_codex":[0.9913815,0.002883294,0.003899508,0.0003528642,0.0009641622,0.0005187207],"domain_scores_gemma":[0.9873709,0.007043523,0.002626264,0.0003576596,0.001230906,0.001370756],"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":[0.002122637,0.001118103,0.005865353,0.001972036,0.001495918,0.0000750185,0.005920863,0.00001000926,0.0001860048,0.006383402,0.08721981,0.8876308],"study_design_scores_gemma":[0.02205818,0.1576154,0.1533587,0.01809792,0.001620841,0.001521696,0.02895281,0.01848449,0.000006480628,0.001685313,0.5960084,0.0005897845],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4808757,0.145197,0.0003233653,0.3693749,0.003504509,0.0004461445,0.00003609308,0.00001656837,0.0002257701],"genre_scores_gemma":[0.8942391,0.09940932,0.00004061242,0.001940047,0.004282158,0.000001393094,0.000003527911,0.00002178824,0.00006207038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.887041,"threshold_uncertainty_score":0.99871,"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."}}