Trajectories of oral bisphosphonate use after hip fractures: a population-based cohort study
Bibliographic record
Abstract
Bisphosphonates prevent future hip fractures. However, we found that one in six patients with hip fractures had a delay in bisphosphonate initiation and another one-sixth discontinued treatment within 12 months after discharge. Our results highlight the need to address hesitancy in treatment initiation and continuous monitoring. Suboptimal antiresorptive use is not well understood. This study investigated trajectories of oral bisphosphonate use following first hip fractures and factors associated with different adherence and persistence trajectories. We conducted a retrospective study of all patients aged ≥ 50 years dispensed two or more bisphosphonate prescriptions following first hip fracture in Victoria, Australia, from 2012 to 2017. Twelve-month trajectories of bisphosphonate use were categorized using group-based trajectory modeling. Factors associated with different trajectories compared to the persistent adherence trajectory were assessed using multivariate multinomial logistic regression. We identified four patterns of oral bisphosphonate use in 1811 patients: persistent adherence (66%); delayed dispensing (17%); early discontinuation (9%); and late discontinuation (9%). Pre-admission bisphosphonate use was associated with a lower risk of delayed dispensing in both sexes (relative risk [RR] 0.28, 95% confidence interval [CI] 0.21–0.39). Older patients ( $$\ge$$ 85 years old versus 50–64 years old, RR 0.38, 95% CI 0.22–0.64) had a lower risk of delayed dispensing. Males with anxiety (RR 9.80, 95% CI 2.24–42.9) and females with previous falls had increased risk of early discontinuation (RR 1.80, 95% CI 1.16–2.78). Two-thirds of patients demonstrated good adherence to oral bisphosphonates over 12 months following hip fracture. Efforts to further increase post-discharge antiresorptive use should be sex-specific and address possible persistent uncertainty around delaying treatment initiation.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".