Association of age, sex and race with prescription of anti-osteoporosis medications following low-energy hip fracture in a retrospective registry cohort
Bibliographic record
Abstract
BACKGROUND: Initiation of anti-osteoporosis medications after hip fracture lowers the risk of subsequent fragility fractures. Historical biases of targeting secondary fracture prevention towards certain groups may result in treatment disparities. We examined associations of patient age, sex and race with anti-osteoporosis medication prescription following hip fracture. METHODS: A cohort of patients with a hip fracture between 2016-2018 was assembled from the American College of Surgeons National Surgical Quality Improvement Program registry. Patients on anti-osteoporosis medications prior to admission were excluded. Multivariable logistic regression was used to determine adjusted associations between patient age, sex and race and their interactions with prescription of anti-osteoporosis medications within 30 days of surgery. RESULTS: In total, 12,249 patients with a hip fracture were identified with a median age of 82 years (IQR: 73-87), and 67% were female (n = 8,218). Thirty days postoperatively, 26% (n = 3146) of patients had been prescribed anti-osteoporosis medication. A significant interaction between age and sex with medication prescription was observed (p = 0.04). Male patients in their 50s (OR:0.75, 95%CI:0.60-0.92), 60s (OR:0.81, 95%CI:0.70-0.94) and 70s (OR:0.89, 95%CI:0.81-0.97) were less likely to be prescribed anti-osteoporosis medication compared to female patients of the same age. Patients who belonged to minority racial groups were not less likely to receive anti-osteoporosis medications than patients of white race. INTERPRETATION: Only 26% of patients were prescribed anti-osteoporosis medications following hip fracture, despite consensus guidelines urging early initiation of secondary prevention treatments. Given that prescription varied by age and sex, strategies to prevent disparities in secondary fracture prevention are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".