{"id":"W1868328676","doi":"10.1186/1472-6963-6-22","title":"Seasonality of service provision in hip and knee surgery: A possible contributor to waiting times? A time series analysis","year":2006,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Health Sciences Centre; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Department of Family and Community Medicine, University of Toronto; University of Toronto","keywords":"Medicine; Knee replacement; Health administration; Seasonality; Benchmarking; Interrupted Time Series Analysis; Public health; Population; Orthopedic surgery; Total hip replacement; Demography; Physical therapy; Surgery; Environmental health; Nursing; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01194251,0.0001867707,0.0008108743,0.0005975198,0.001306759,0.00006246842,0.0001940462,0.000252417,0.0002685658],"category_scores_gemma":[0.0002830372,0.0001735787,0.00006550535,0.005386489,0.00004387746,0.000334867,0.0002375073,0.0006271272,0.00009504989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000427971,"about_ca_system_score_gemma":0.003158892,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06445138,"about_ca_topic_score_gemma":0.06086491,"domain_scores_codex":[0.9920859,0.003888727,0.001443935,0.0005741722,0.0008188242,0.001188472],"domain_scores_gemma":[0.9946447,0.002208986,0.0003228922,0.0004669742,0.001904794,0.0004516567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004008655,0.000153758,0.9824162,0.0064256,0.00002962616,0.000002493669,0.005952497,0.00233712,0.0001486612,0.0009630131,0.0001658682,0.001004278],"study_design_scores_gemma":[0.0006743617,0.0001679057,0.821233,0.001324257,0.00001754628,9.046534e-7,0.005468395,0.169776,0.00003659523,0.0002062718,0.0009010845,0.0001936735],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874506,0.0005359181,0.0004017745,0.008620755,0.00004191068,0.002396002,0.0001612258,0.00005990815,0.000331912],"genre_scores_gemma":[0.9895952,0.00006081429,0.007017924,0.001503461,0.0001535642,0.0003333772,0.0004389245,0.00003167728,0.000865006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1674389,"threshold_uncertainty_score":0.9999934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06845080108476251,"score_gpt":0.4607608409463206,"score_spread":0.3923100398615581,"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."}}