Impact of the COVID-19 Pandemic on Timeliness of Denosumab Dispensations in Ontario, Canada: An Interrupted Time Series Analysis
Bibliographic record
Abstract
Denosumab is an effective osteoporosis medication, yet delays in therapy are associated with increased risk of multiple vertebral fractures. In this thesis, we conducted an interrupted time series analysis to estimate the impact of the COVID-19 pandemic on the proportion of patients who received on-time denosumab dispensations (within 183 +/- 30 days of prior dispensation). Analyses were stratified by 1) patient residence at denosumab dispensation (community or long-term care), and 2) prior denosumab use. We found an immediate decline in the proportion of on-time dispensations in March 2020 in the community (-17.8% [95% CI: -19.6, -16.0]) and in long-term care (-3.2 % [95% CI: -5.0, -1.4]). A decline was seen regardless of prior denosumab use. The proportion of on-time denosumab dispensations returned to pre-pandemic levels by Fall 2020. Future research on vertebral fracture rates, reasons for denosumab delays, and strategies for fracture prevention is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".