{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009915228,0.0006583987,0.000452174,0.0009994046,0.0009775916,0.002299801,0.001502754,0.0008797018,0.003666944],"category_scores_gemma":[0.04074412,0.0003715007,0.001351441,0.000432184,0.0006848632,0.001859097,0.001776526,0.001223879,0.00108342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00106096,"about_ca_system_score_gemma":0.002501623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004708102,"about_ca_topic_score_gemma":0.004487352,"domain_scores_codex":[0.9930238,0.00397794,0.0006380994,0.0004318705,0.001279863,0.0006484888],"domain_scores_gemma":[0.9673898,0.02383663,0.0020713,0.00106641,0.00385227,0.001783593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","study_design_scores_codex":[0.008579616,0.03704702,0.3415185,0.006301074,0.0003706023,0.004117629,0.03220391,0.001291822,0.005985078,0.001084496,0.0127594,0.5487409],"study_design_scores_gemma":[0.003482248,0.1312117,0.7161155,0.004911593,0.00278201,0.005258263,0.06787872,0.01714461,0.01220658,0.001505419,0.03681099,0.0006923676],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869663,0.0005354789,0.0008164943,0.00171005,0.0001195667,0.002802682,0.0003884057,0.0001551441,0.00650603],"genre_scores_gemma":[0.9866253,0.001031406,0.007315311,0.0009436235,0.0001053248,0.002310714,0.0003057543,0.00002499446,0.001337554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009915228,"threshold_uncertainty_score":0.05243737,"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."}}