{"id":"W2346116156","doi":"10.21037/mhealth.2016.03.01","title":"Text messaging app improves disease surveillance in rural South Sudan","year":2016,"lang":"en","type":"article","venue":"mHealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Global Affairs Canada; Department for International Development; Department of Foreign Affairs and Trade, Australian Government; European Commission","keywords":"Android (operating system); Short Message Service; mHealth; Internet privacy; Phone; Business; Health care; Medical emergency; Medicine; Computer science; Nursing; Psychological intervention; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001333204,0.0003190825,0.0002271144,0.0004969408,0.0008132023,0.0008796469,0.000352386,0.0002930162,0.003647168],"category_scores_gemma":[0.004016984,0.0001122188,0.0002950204,0.0003327939,0.0001693221,0.0006127452,0.0007469609,0.0002980048,0.0006484152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004559011,"about_ca_system_score_gemma":0.001067453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00806893,"about_ca_topic_score_gemma":0.0123251,"domain_scores_codex":[0.9993851,0.0003449004,0.0000357559,0.00005457929,0.00006802745,0.0001115638],"domain_scores_gemma":[0.9990489,0.0004088801,0.0001702615,0.00003424448,0.0001931619,0.0001445455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002140904,0.004577129,0.330815,0.003072042,0.0001852138,0.001682787,0.01885538,0.0008810102,0.0173322,0.0006569603,0.0244804,0.5953209],"study_design_scores_gemma":[0.0003379501,0.007842846,0.8554666,0.002385108,0.0004800121,0.0009408655,0.04011811,0.004602341,0.008901656,0.001139992,0.07766351,0.000120973],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886885,0.000854981,0.0005601119,0.001931442,0.0001286716,0.000267265,0.0006318614,0.0001556139,0.006781454],"genre_scores_gemma":[0.9906437,0.001097925,0.003719008,0.0009398363,0.00005290985,0.000258926,0.0004707962,0.00001643513,0.00280046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00806893,"threshold_uncertainty_score":0.0160439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0279684996353467,"score_gpt":0.3869493670836461,"score_spread":0.3589808674482994,"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."}}