Real-world differences in denosumab persistence, reinitiation, and switching among cohorts of older adults in Canada and the United States
Bibliographic record
Abstract
Denosumab is an injectable osteoporosis medication administered twice per year. Discontinuation of denosumab can result in rapid rebound fractures, but the evidence is limited on real-world persistence with denosumab. We conducted 2 parallel, population-based cohort studies leveraging (1) healthcare administrative data from Ontario, Canada (ON; 100% population) and (2) a 20% random sample of US Medicare beneficiaries (US). The first denosumab claim (US: 1/2010-12/2019; ON: 1/2012-12/2021) was identified using pharmacy claims (ON) and Medicare Parts D and B claims (US). Patients aged <66 yr, residing in long-term care (LTC), or with implausible data (eg, death before first claim) were excluded. We developed and applied an algorithm that used dosing and days between dispensations to clean denosumab claims. We assumed a days supply of 183 d for each dispensation and defined discontinuation as a 60-d gap in coverage. We estimated initial persistence, reinitiation, and switching to other osteoporosis medications using Kaplan-Meier estimators, censoring on death, disenrollment (US only), LTC admission, or study end (12/31/2022 [ON], 12/31/2020 [US]). We also estimated the monthly proportion of patients with an on-time denosumab dose to explore time trends. We identified 168 339 eligible individuals in ON (mean age = 78 yr; 90% female) and 97 595 in the US (mean age = 77 yr; 90% female). In ON, the median time to denosumab discontinuation was longer (median 2.3 yr [ON] vs 1.7 yr [US]; 3-yr persistence: 44% [ON] vs 31% [US]), and time to reinitiation was shorter (median = 0.5 yr [ON] vs 1.9 yr [US]). In both populations, around 10% switched to another osteoporosis medication. Women and those with prior oral bisphosphonate use had longer durations of denosumab treatment in ON but not in the US. The proportion persisting with on-time doses did not increase over time in the US or ON. Research to improve persistence with denosumab and optimize post-denosumab treatment is critical.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".