{"id":"W2970394565","doi":"10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a5167","title":"An Individual Patient Data Meta-Analysis to Estimate the Diagnostic Accuracy of a Machine Learning-Based Software for Analyzing Chest X-Rays of Persons with Symptoms of Pulmonary Tuberculosis: Preliminary Findings","year":2019,"lang":"en","type":"article","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Pulmonary tuberculosis; Patient data; Software; Meta-analysis; Computer science; Medicine; Machine learning; Artificial intelligence; Data mining; Tuberculosis; Internal medicine; Programming language; Pathology; Database","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":[0.02858518,0.003501289,0.008044153,0.002620419,0.0007825739,0.003520482,0.002160202,0.00314729,0.002594127],"category_scores_gemma":[0.07500947,0.001693343,0.04363857,0.002628313,0.0008181585,0.001882188,0.0019042,0.003147234,0.000463902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001124856,"about_ca_system_score_gemma":0.0009727682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004262412,"about_ca_topic_score_gemma":0.005087603,"domain_scores_codex":[0.9731541,0.01910012,0.002235647,0.003624144,0.001414482,0.0004715189],"domain_scores_gemma":[0.9451458,0.04231435,0.003434071,0.006786491,0.00181959,0.0004995833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.02335604,0.00006053975,0.02579005,0.004115393,0.9398894,0.0001239046,0.00006597394,0.001268932,0.0008179168,0.0001186572,0.0003085013,0.004084724],"study_design_scores_gemma":[0.003811681,0.001308174,0.01193022,0.0002968926,0.9786561,0.0001879818,0.0000360432,0.002195168,0.0003836622,0.0004296083,0.0007254275,0.00003904082],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6027479,0.3243356,0.05994676,0.001559657,0.002161362,0.001170208,0.005100286,0.001015901,0.001962336],"genre_scores_gemma":[0.9777421,0.007795785,0.01095936,0.000840384,0.0002862029,0.0004282847,0.001236882,0.0001751906,0.0005358874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02858518,"threshold_uncertainty_score":0.1511747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02753823538583563,"score_gpt":0.3244169623306955,"score_spread":0.2968787269448599,"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."}}