{"id":"W3047721186","doi":"10.30953/tmt.v5.177","title":"Feasibility and Effectiveness of Mobile App for Active Case Finding for Tuberculosis in India","year":2020,"lang":"en","type":"article","venue":"Telehealth and Medicine Today","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Medicine; Tuberculosis; Slum; Case finding; Population; Mobile apps; Incidence (geometry); Pulmonary tuberculosis; Environmental health; Infectious disease (medical specialty); Pediatrics; Disease; Family medicine; Internal medicine; Pathology; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002763045,0.0001930027,0.0008041685,0.0001436445,0.0005586047,0.000001229163,0.00006476438,0.0002196926,0.00002962812],"category_scores_gemma":[0.001397798,0.0001603496,0.00003623602,0.0003241605,0.0001475516,0.00006653291,0.00004125184,0.0004180828,0.000001316902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001671959,"about_ca_system_score_gemma":0.0006209651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001028832,"about_ca_topic_score_gemma":0.0002275528,"domain_scores_codex":[0.9973326,0.0004322361,0.0009153009,0.0005173539,0.0001179537,0.0006844995],"domain_scores_gemma":[0.9927071,0.005687064,0.0003452227,0.0002014177,0.0001761653,0.0008830203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01290814,0.0004833184,0.3929541,0.1588546,0.00006844434,0.00002687692,0.03800705,0.000009857073,0.001098834,0.02726316,0.00673055,0.3615951],"study_design_scores_gemma":[0.05382022,0.01009023,0.7404133,0.004441724,0.0002968448,0.0001051514,0.03085335,0.002403375,0.0004965065,0.00711144,0.1491468,0.0008210005],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.96706,0.003174586,0.002334574,0.003823427,0.0001984337,0.02275802,0.0003129205,0.00005810131,0.0002799027],"genre_scores_gemma":[0.9740811,0.001163631,0.0008987295,0.003065083,0.0002845615,0.0203868,0.00007974795,0.00002708254,0.000013253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3607741,"threshold_uncertainty_score":0.6538867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06998263623175105,"score_gpt":0.4500298635099544,"score_spread":0.3800472272782034,"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."}}