{"id":"W4396884932","doi":"10.17760/d20659815","title":"An analysis of database management systems in mitigating patient misidentification through unique patient identifiers","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Identifier; Identification (biology); Health care; Computer science; Set (abstract data type); Work (physics); Database; Data science; Risk analysis (engineering); Computer security; Medicine; Engineering","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":[0.01672797,0.0005062253,0.0004233081,0.002477726,0.00134121,0.006224043,0.00106088,0.0009243239,0.003934292],"category_scores_gemma":[0.04922609,0.0003048969,0.0008840903,0.002571453,0.0007354768,0.006124148,0.001611114,0.001033452,0.0005833733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004078767,"about_ca_system_score_gemma":0.005206638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005588881,"about_ca_topic_score_gemma":0.004593818,"domain_scores_codex":[0.9855145,0.009261557,0.0006488213,0.0006333804,0.003163176,0.0007785172],"domain_scores_gemma":[0.9385297,0.04153626,0.005190556,0.00239544,0.01137098,0.0009770921],"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":[0.002160532,0.002329316,0.2397874,0.003099013,0.0007868915,0.0004039106,0.004564293,0.07561694,0.005715907,0.13813,0.01525103,0.5121547],"study_design_scores_gemma":[0.0004108398,0.006902502,0.2195393,0.004151543,0.002109465,0.000683465,0.03836002,0.5641755,0.02139485,0.05395395,0.08799879,0.0003196973],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8320744,0.005905002,0.05005883,0.01929176,0.0003458007,0.001638605,0.0009824489,0.0003575809,0.08934562],"genre_scores_gemma":[0.9653921,0.002050634,0.02849473,0.0004927712,0.00005533695,0.0002130582,0.0003270379,0.00002600692,0.002948183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01672797,"threshold_uncertainty_score":0.088467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.041760353309563,"score_gpt":0.4419558001091552,"score_spread":0.4001954467995922,"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."}}