{"id":"W4293089519","doi":"10.1093/cid/ciac679","title":"The Performance of Computer-Aided Detection Digital Chest X-ray Reading Technologies for Triage of Active Tuberculosis Among Persons With a History of Previous Tuberculosis","year":2022,"lang":"en","type":"article","venue":"Clinical Infectious Diseases","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Allergy and Infectious Diseases; Government of Canada","keywords":"Medicine; Tuberculosis; Triage; Reading (process); Active tuberculosis; Medical emergency; Mycobacterium tuberculosis; Pathology; Linguistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004142471,0.0002045323,0.0007454978,0.0002107276,0.0002025127,0.00001221357,0.0001894904,0.0001033784,0.00001172494],"category_scores_gemma":[0.002311466,0.0001547329,0.0004967219,0.0004015661,0.001023684,0.0001637986,0.0001732817,0.0003307689,4.003383e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005648814,"about_ca_system_score_gemma":0.0003754107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009728924,"about_ca_topic_score_gemma":0.00002462967,"domain_scores_codex":[0.9981127,0.0001366293,0.0007905524,0.0003939725,0.000335532,0.0002306242],"domain_scores_gemma":[0.9943514,0.004034176,0.000723408,0.0005291295,0.0002758838,0.00008594579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002505352,0.003857108,0.8538473,0.001508497,0.001347543,0.000005760677,0.0006189083,0.001338825,0.0008579101,0.00001850654,0.002049125,0.1320452],"study_design_scores_gemma":[0.007918308,0.02392805,0.9328139,0.001006681,0.003780146,0.00002285572,0.001968764,0.01433896,0.005505809,0.00006383754,0.008096028,0.0005566804],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996806,0.000568548,0.000269712,0.0004444401,0.0003006628,0.001208575,0.0001465735,0.0002065651,0.00004891908],"genre_scores_gemma":[0.9988636,0.0003025285,0.00005120999,0.0001284032,0.00006784373,0.0004832214,0.00001978202,0.00003251092,0.00005094922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1314885,"threshold_uncertainty_score":0.6309824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02693120912796855,"score_gpt":0.3048236146895899,"score_spread":0.2778924055616213,"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."}}