On-time denosumab dosing recovered rapidly during the COVID-19 pandemic, yet remains suboptimal
Bibliographic record
Abstract
Timely administration of denosumab every 6 mo is critical in osteoporosis treatment to avoid multiple vertebral fracture risk upon denosumab discontinuation or delay. This study aimed to estimate the immediate and prolonged impact of the COVID-19 pandemic on the timing of denosumab doses. We identified older adults (≥66 yr) residing in the community who were due to receive denosumab between January 2016 and December 2020 using Ontario Drug Benefit data. We completed an interrupted time-series analysis to estimate the impact of the COVID-19 pandemic (March 2020) on the monthly proportion of on-time denosumab doses (183 +/-30 d). Analyses were stratified by user type: patients due for their second dose (novice users), third or fourth dose (intermediate users), or ≥5th dose (established users). In additional analyses, we considered patients living in nursing homes, switching to other osteoporosis drugs, and reported trends until February 2022. We studied 148 554 patients (90.9% female, mean [SD] age 79.6 [8.0] yr) receiving 648 221 denosumab doses. The average pre-pandemic proportion of on-time therapy was steady in the community, yet differed by user type: 64.9% novice users, 72.3% intermediate users, and 78.0% established users. We identified an immediate overall decline in the proportion of on-time doses across all user types at the start of the pandemic: -17.8% (95% CI, -19.6, -16.0). In nursing homes, the pre-pandemic proportion of on-time therapy was similar across user types (average 83.5%), with a small decline at the start of the pandemic: -3.2% (95% CI, -5.0, -1.2). On-time therapy returned to pre-pandemic levels by October 2020 and was not impacted by therapy switching. Although on-time dosing remains stable as of February 2022, approximately one-fourth of patients in the community do not receive denosumab on-time. In conclusion, although pandemic disruptions to denosumab dosing were temporary, levels of on-time therapy remain suboptimal.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".