{"id":"W4409767545","doi":"10.1186/s12916-025-04062-6","title":"When health data go dark: the importance of the DHS Program and imagining its future","year":2025,"lang":"en","type":"article","venue":"BMC Medicine","topic":"Global Maternal and Child Health","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Medicine; Agency (philosophy); Documentation; Population; Global health; Data collection; Survey data collection; Economic growth; Environmental health; Public health; Nursing","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":[0.0007959986,0.0001188311,0.0003392478,0.0000284856,0.0001264015,0.00000556943,0.0003714343,0.00003642328,0.00004111358],"category_scores_gemma":[0.0002192641,0.00004997166,0.00002359871,0.0002069622,0.0001922087,0.00003863292,0.00025469,0.0002577241,0.000001738791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000273714,"about_ca_system_score_gemma":0.0003712116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002460862,"about_ca_topic_score_gemma":0.0003432321,"domain_scores_codex":[0.9987558,0.00007693493,0.0003832577,0.0002576017,0.000292621,0.0002337598],"domain_scores_gemma":[0.9986997,0.00007111667,0.0001760051,0.0008722773,0.00007073279,0.0001101689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001662014,0.0001065198,0.7100555,0.002172719,0.00006306617,0.000006276669,0.001352006,2.313058e-7,0.00008948273,0.006812065,0.2416153,0.0375607],"study_design_scores_gemma":[0.00178515,0.0002812665,0.7405195,0.001873119,0.0001133019,0.00007776808,0.001553264,0.0002122305,0.00001717507,0.001059878,0.2524503,0.00005710754],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2656255,0.05245518,0.0000836157,0.6608739,0.001351517,0.002324831,0.00008119348,0.00009498146,0.01710933],"genre_scores_gemma":[0.941498,0.002126629,0.001540471,0.05053113,0.001407556,0.00002399902,0.00008951094,0.0000177294,0.002764979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6758725,"threshold_uncertainty_score":0.2037785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03882595011702442,"score_gpt":0.3714444083107669,"score_spread":0.3326184581937425,"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."}}