{"id":"W4414346850","doi":"10.1093/biomtc/ujaf116","title":"Estimating associations between cumulative exposure and health via generalized distributed lag non-linear models using penalized splines","year":2025,"lang":"en","type":"article","venue":"Biometrics","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Health Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalized additive model; Lag; Distributed lag; Scale (ratio); Laplace's method; Generalized linear model; Generalized linear mixed model; Additive model","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.007964259,0.0008557323,0.00111669,0.001223073,0.0003838318,0.001037248,0.002189768,0.00108086,0.003200312],"category_scores_gemma":[0.02541274,0.0005757295,0.002334659,0.001789908,0.001072825,0.001115826,0.00188156,0.002612988,0.0005319795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006216597,"about_ca_system_score_gemma":0.001954296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01186986,"about_ca_topic_score_gemma":0.01434847,"domain_scores_codex":[0.9961897,0.002714894,0.0001328793,0.0005000883,0.0003179551,0.0001446478],"domain_scores_gemma":[0.9862264,0.0115077,0.0007292919,0.0008252005,0.0005585423,0.0001529068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002539949,0.0001354259,0.02052473,0.0003011706,0.0003974907,0.0003574477,0.0002934723,0.7926345,0.001373428,0.06784365,0.001864511,0.1140201],"study_design_scores_gemma":[0.00002575886,0.00006351354,0.001916497,0.0000285709,0.00003554235,0.00004544323,0.00003622494,0.9650437,0.000225328,0.03148841,0.001069774,0.00002129041],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01463047,0.0001563653,0.9843179,0.0001814122,0.00002570618,0.00004609578,0.0001752742,0.0001985087,0.0002683206],"genre_scores_gemma":[0.5182583,0.0008854346,0.4738192,0.0002399463,0.0001335311,0.0005876425,0.001446247,0.0001770683,0.004452614],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01186986,"threshold_uncertainty_score":0.0421195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1720925525237839,"score_gpt":0.4147274843414834,"score_spread":0.2426349318176995,"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."}}