{"id":"W3016291603","doi":"10.5588/ijtld.19.0284","title":"ScreenTB: a tool for prioritising risk groups and selecting algorithms for screening for active tuberculosis","year":2020,"lang":"en","type":"article","venue":"The International Journal of Tuberculosis and Lung Disease","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"World Health Organization","keywords":"Prioritization; False positive paradox; Tuberculosis; Context (archaeology); Medical diagnosis; Medicine; Risk analysis (engineering); Risk assessment; Computer science; Machine learning; Management science; Computer security; 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":[0.004214407,0.001808206,0.001019656,0.005924831,0.0007198591,0.002027156,0.001290623,0.001085888,0.02397876],"category_scores_gemma":[0.0331153,0.0006589272,0.001469868,0.001699919,0.0003017143,0.001886679,0.00179577,0.000909692,0.002937694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092285,"about_ca_system_score_gemma":0.0024082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006597868,"about_ca_topic_score_gemma":0.009426066,"domain_scores_codex":[0.9975334,0.001277327,0.0003327303,0.0002263983,0.0005273439,0.0001027147],"domain_scores_gemma":[0.9790176,0.01642315,0.002004153,0.000466596,0.001529748,0.000558948],"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":[0.002388381,0.001199666,0.1325926,0.002731337,0.001150138,0.0006903622,0.001282824,0.03893232,0.00370917,0.007838354,0.2167881,0.5906968],"study_design_scores_gemma":[0.002359423,0.001699449,0.0824853,0.003192839,0.001299724,0.002738291,0.001395686,0.7175122,0.01175821,0.03329155,0.1416833,0.0005840986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.132607,0.001982745,0.7090819,0.007551103,0.0004738525,0.004409294,0.04175106,0.08131947,0.02082352],"genre_scores_gemma":[0.3185467,0.0008223421,0.6562687,0.001280661,0.0001667706,0.003027832,0.01340151,0.001919563,0.004565886],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02397876,"threshold_uncertainty_score":0.080217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03764828411685544,"score_gpt":0.351245163159941,"score_spread":0.3135968790430856,"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."}}