{"id":"W4200434450","doi":"10.1093/ofid/ofab567","title":"Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence–Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis","year":2021,"lang":"en","type":"article","venue":"Open Forum Infectious Diseases","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Canadian Institutes of Health Research","keywords":"Triage; Medicine; GeneXpert MTB/RIF; Chest radiograph; Tuberculosis; Cost effectiveness; Referral; Medical emergency; Emergency medicine; Radiology; Family medicine; Sputum; Radiography; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002795295,0.0003164822,0.0009599786,0.0007717279,0.0002530692,0.0002306111,0.0001939558,0.0001004967,0.0004806168],"category_scores_gemma":[0.000551157,0.00028862,0.000670473,0.0039934,0.0002472165,0.0002904916,0.0001349477,0.0001633719,0.000005959233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003903754,"about_ca_system_score_gemma":0.0008994137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001299503,"about_ca_topic_score_gemma":0.0006988095,"domain_scores_codex":[0.9977459,0.0003523289,0.0004717715,0.0006586465,0.0004062775,0.0003650615],"domain_scores_gemma":[0.9974088,0.0009067549,0.0002538992,0.0006655938,0.0004854487,0.0002794888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002027939,0.00367451,0.930474,0.0005387677,0.005607742,0.0002090257,0.0002777689,0.03985641,0.001519811,0.0008412164,0.00009636096,0.01487649],"study_design_scores_gemma":[0.009053227,0.004683062,0.5578898,0.005063935,0.06867755,0.0002238149,0.00363858,0.2362563,0.1088693,0.002288516,0.0009976313,0.002358268],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8833045,0.0006014178,0.1104296,0.002095412,0.0002057057,0.002517007,0.0002859286,0.0001558263,0.0004045571],"genre_scores_gemma":[0.9969974,0.00001580797,0.0006109981,0.001702403,0.00003890837,0.0003443165,0.0002311765,0.00004895814,0.00001004135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3725842,"threshold_uncertainty_score":0.9999566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0359377080733052,"score_gpt":0.3513298306282895,"score_spread":0.3153921225549843,"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."}}