{"id":"W2413786735","doi":"10.1371/journal.pmed.1002034","title":"Early Childhood Developmental Status in Low- and Middle-Income Countries: National, Regional, and Global Prevalence Estimates Using Predictive Modeling","year":2016,"lang":"en","type":"article","venue":"PLoS Medicine","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":560,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Grand Challenges Canada; Wellcome Trust","keywords":"Low and middle income countries; Environmental health; Medicine; Developing country; Global health; Predictive power; Low income; Demography; Pediatrics; Public health; Socioeconomics; Economic growth; Economics; Pathology","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":[0.01088072,0.001045946,0.0009790643,0.004171518,0.0004984019,0.001653815,0.00147795,0.0006848002,0.001623693],"category_scores_gemma":[0.02316216,0.0007862059,0.002416888,0.004697966,0.0008938002,0.001238484,0.002425974,0.00146082,0.0003553566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008266073,"about_ca_system_score_gemma":0.0009323046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03391385,"about_ca_topic_score_gemma":0.01207478,"domain_scores_codex":[0.9969382,0.001694726,0.0001987621,0.0007161595,0.0002854403,0.0001667463],"domain_scores_gemma":[0.9876958,0.007657591,0.002029175,0.00142602,0.0009466938,0.0002447736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006268873,0.00004741706,0.9500943,0.0001013057,0.000815072,0.0001102042,0.0003132888,0.03528254,0.00006577935,0.001003526,0.0007841918,0.01131965],"study_design_scores_gemma":[0.0000380048,0.0001184495,0.6031237,0.0005538744,0.0008832063,0.0005434426,0.001836818,0.3856465,0.0002439647,0.005148824,0.001806338,0.0000568625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9449286,0.002515431,0.0454925,0.0005559235,0.00003924821,0.0001209915,0.003986988,0.0002872823,0.002073095],"genre_scores_gemma":[0.9877037,0.0008919231,0.007521792,0.00004028811,0.00002295594,0.00009416432,0.003600839,0.00003003043,0.00009435686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03391385,"threshold_uncertainty_score":0.06743288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03454249672576005,"score_gpt":0.2823719839567514,"score_spread":0.2478294872309914,"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."}}