{"id":"W3007256858","doi":"10.1016/j.jclinepi.2020.02.006","title":"Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review","year":2020,"lang":"en","type":"review","venue":"Journal of Clinical Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":143,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University; Ottawa Hospital; University of Ottawa","funders":"National Health and Medical Research Council; Canadian Institutes of Health Research","keywords":"Interrupted time series; Public health; Series (stratigraphy); Psychological intervention; Public health interventions; Interrupted Time Series Analysis; Statistics; Statistical analysis; Time series; Medicine; Computer science; Mathematics; Nursing","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","metaepi_narrow","metaepi_broad","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1747523,0.0005847879,0.02317983,0.0003155101,0.00005224731,0.00001853177,0.0005539929,0.0005594675,0.0001044866],"category_scores_gemma":[0.7437028,0.0003987795,0.001358416,0.0003667121,0.0006060192,0.0002341321,0.000432117,0.003277421,0.00001090569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003078871,"about_ca_system_score_gemma":0.001299616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001015382,"about_ca_topic_score_gemma":0.00000177221,"domain_scores_codex":[0.8494585,0.1198656,0.02874371,0.0007923059,0.0003547757,0.0007851194],"domain_scores_gemma":[0.6084783,0.361866,0.0275171,0.0005993773,0.0008546184,0.0006846446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002776291,0.00009855527,0.00002263775,0.06197217,0.0005690089,0.0000458315,0.00005619362,4.476576e-8,4.601479e-8,0.005292635,0.002555463,0.9293597],"study_design_scores_gemma":[0.000468656,0.005571263,0.00004362075,0.3518049,0.001759915,0.0008239478,0.00005749261,0.0001713552,4.520555e-8,0.3539711,0.2848096,0.0005180893],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[3.530727e-7,0.5678107,0.4279819,0.003261511,0.0001940262,0.0007009543,0.00001750561,0.00003029294,0.000002767206],"genre_scores_gemma":[1.596942e-7,0.5239828,0.4750422,0.0007330636,0.0001382416,0.00004936329,0.000009703513,0.00003300451,0.00001140796],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9288415,"threshold_uncertainty_score":0.9998464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9578941774010222,"score_gpt":0.7853317705012486,"score_spread":0.1725624068997736,"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."}}