{"id":"W4396884953","doi":"10.17760/d20653169","title":"Enhancing anesthesia safety: evaluating artificial intelligence and augmented reality in reducing medication errors","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Patient Safety and Medication Errors","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Situation awareness; Augmented reality; Context (archaeology); Patient safety; Medicine; Clinical decision support system; Situational ethics; Health care; Adverse effect; Intensive care medicine; Medical emergency; Computer science; Risk analysis (engineering); Decision support system; Artificial intelligence; Psychology; Engineering; Pharmacology","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":[0.003496281,0.0005081909,0.0004682147,0.0007055649,0.0003096325,0.001834427,0.000477159,0.0007579618,0.004723753],"category_scores_gemma":[0.01571073,0.0001834999,0.001016407,0.0004102797,0.0004475982,0.001260259,0.0007920027,0.000606301,0.0005303786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006894716,"about_ca_system_score_gemma":0.001118671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001048747,"about_ca_topic_score_gemma":0.001440842,"domain_scores_codex":[0.9979964,0.001136351,0.00013781,0.0001489399,0.0004894077,0.00009107697],"domain_scores_gemma":[0.9904631,0.007194939,0.0008409618,0.0002644637,0.0009503018,0.0002863357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008640856,0.01610818,0.03126778,0.005184852,0.001012092,0.00008325936,0.001789469,0.03553794,0.008797502,0.005286991,0.003538142,0.8827528],"study_design_scores_gemma":[0.006802554,0.2025724,0.2715077,0.009180901,0.007854148,0.0006975386,0.01584991,0.3711696,0.04855322,0.01607947,0.04917305,0.0005595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9432005,0.005388335,0.01515438,0.001440282,0.0002542947,0.003001957,0.0002843683,0.0001604464,0.03111539],"genre_scores_gemma":[0.9485563,0.003987426,0.04288704,0.0003820174,0.0000573069,0.0009035063,0.0002159916,0.00001665488,0.002993674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004723753,"threshold_uncertainty_score":0.01849031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1527500523665358,"score_gpt":0.489894967757338,"score_spread":0.3371449153908022,"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."}}