{"id":"W4306809583","doi":"10.1101/2022.10.17.22281160","title":"Evaluation of statistical methods used to meta-analyse results from interrupted time series studies: a simulation study","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Monash University; Australian Government","keywords":"Statistics; Autocorrelation; Meta-analysis; Context (archaeology); Random effects model; Econometrics; Restricted maximum likelihood; Series (stratigraphy); Ordinary least squares; Mathematics; Variance (accounting); Maximum likelihood; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3600362,0.003248401,0.008851294,0.005711271,0.000850913,0.005593709,0.004519228,0.004560155,0.006695825],"category_scores_gemma":[0.6199834,0.002058448,0.02920831,0.006358995,0.001661406,0.003197031,0.002882549,0.004931307,0.0007776294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004202817,"about_ca_system_score_gemma":0.005627919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003189503,"about_ca_topic_score_gemma":0.002224019,"domain_scores_codex":[0.5329741,0.4382364,0.01631828,0.004717162,0.007041049,0.0007129372],"domain_scores_gemma":[0.2489249,0.6965739,0.01990244,0.02125748,0.01256405,0.0007772247],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01543484,0.0005215306,0.02186271,0.08105628,0.3747579,0.001193736,0.001149907,0.2908825,0.000968977,0.03830538,0.01127224,0.1625941],"study_design_scores_gemma":[0.01422888,0.005621741,0.008146475,0.03626206,0.227524,0.001201409,0.0004853104,0.5486467,0.002963621,0.1236398,0.0306441,0.0006357973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01799391,0.04806025,0.9070852,0.004289567,0.001562349,0.01375569,0.003153292,0.001739018,0.002360777],"genre_scores_gemma":[0.3468712,0.01555952,0.5949965,0.002156445,0.0004587184,0.03691775,0.001773674,0.0005224509,0.0007437672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6399639,"threshold_uncertainty_score":0.7891893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9312262165821488,"score_gpt":0.6812826678010853,"score_spread":0.2499435487810635,"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."}}