{"id":"W2967429473","doi":"10.1016/j.jneb.2019.07.013","title":"Use of Survival Analysis to Predict Attrition Among Women Participating in Longitudinal Community-Based Nutrition Research","year":2019,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"U.S. Department of Agriculture","keywords":"Attrition; Longitudinal study; Odds; Supplemental Nutrition Assistance Program; Gerontology; Observational study; Medicine; Odds ratio; Hazard ratio; Confidence interval; Multivariate analysis; Demography; Nutrition Education; Proportional hazards model; Environmental health; Logistic regression; Geography; Food security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01121007,0.0004429357,0.000559073,0.001543379,0.0008771699,0.0009823968,0.0008883127,0.0007814119,0.001246843],"category_scores_gemma":[0.0194122,0.0002753174,0.001243653,0.001122278,0.0002551813,0.001048549,0.0009152588,0.0009924661,0.0002783551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004054725,"about_ca_system_score_gemma":0.001258712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006762678,"about_ca_topic_score_gemma":0.00773977,"domain_scores_codex":[0.9976923,0.001391663,0.0002215941,0.0002074924,0.0002471074,0.0002397835],"domain_scores_gemma":[0.9846193,0.009245703,0.002159047,0.001660252,0.001597854,0.0007179037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004361401,0.0001260786,0.9947922,0.000005884666,0.00008025023,0.00001211001,0.00009434755,0.0001276455,0.00006946191,0.00003060762,0.000134452,0.004090744],"study_design_scores_gemma":[0.00009496381,0.001262669,0.9744975,0.00002926312,0.000332659,0.00009802006,0.0008388071,0.0215021,0.0003839213,0.0003445075,0.0005978085,0.00001771137],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977638,0.00007232874,0.001282111,0.00006705691,0.00001591386,0.00004072537,0.0004757592,0.00001758727,0.0002647111],"genre_scores_gemma":[0.9976529,0.00004732195,0.0009770283,0.00002867149,0.00001148764,0.0001107585,0.0007402646,0.000007059203,0.0004242928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01121007,"threshold_uncertainty_score":0.05928522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4811287650812767,"score_gpt":0.5525393937861393,"score_spread":0.07141062870486259,"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."}}