{"id":"W7133039087","doi":"","title":"Impact of the COVID-19 Pandemic on Timeliness of Denosumab Dispensations in Ontario, Canada: An Interrupted Time Series Analysis","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Denosumab; Pandemic; Osteoporosis; Interrupted time series; Interrupted Time Series Analysis; Residence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002910718,0.0002922481,0.0005890033,0.00078879,0.0007183343,0.001204588,0.001289227,0.0005742577,0.00291948],"category_scores_gemma":[0.01291196,0.0002356509,0.001034581,0.002473702,0.0005949252,0.0003119225,0.000720053,0.001199677,0.0002049041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01820702,"about_ca_system_score_gemma":0.02312425,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.980271,"about_ca_topic_score_gemma":0.9596592,"domain_scores_codex":[0.9981405,0.0003633306,0.0001388442,0.0002935741,0.0005424598,0.0005212849],"domain_scores_gemma":[0.9932404,0.001727998,0.002142874,0.0003731514,0.001711188,0.0008044612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006284433,0.00007029876,0.9776023,0.0001193035,0.0006042765,0.0001530177,0.001071532,0.005246241,0.0001612693,0.001187287,0.004321733,0.008834124],"study_design_scores_gemma":[0.00005956628,0.0001054678,0.9754902,0.00008589922,0.0002353197,0.00004500074,0.001094134,0.01952072,0.00009159897,0.0002390567,0.003001143,0.00003183206],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770012,0.001379529,0.00124099,0.002041097,0.00005089356,0.00009428742,0.01588325,0.00003330028,0.002275453],"genre_scores_gemma":[0.9870769,0.0008581165,0.0006908933,0.0002427334,0.00002595926,0.0000661216,0.007892244,0.00001209209,0.003134951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01972902,"threshold_uncertainty_score":0.1321018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05821408577581677,"score_gpt":0.4393620907683219,"score_spread":0.3811480049925051,"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."}}