994 CURRENT SECONDARY PREVENTION AFTER HIP FRACTURE IN ENGLAND AND WALES—RESULTS FROM THE NATIONAL HIP FRACTURE DATABASE (NHFD)
Bibliographic record
Abstract
Abstract Introduction National clinical audit seeks to enhance the quality of care of the 75,000 people who break their hip in the UK each year. A key aim for the National Hip Fracture Database (NHFD) is to encourage secondary fracture prevention through bone health assessment and the appropriate provision of anti-osteoporosis medication (AOM). We set out to describe trends in anti-osteoporosis medication prescription, and to examine the types of oral and injectable AOM being prescribed both before and after a hip fracture. Method We used data freely available from the NHFD www.nhfd.co.uk to analyse trends in oral and injectable AOM prescription across a quarter of a million patients presenting between 2016 and 2020, and more detailed information on the individual type of AOM prescribed for 63,284 patients from 171 hospitals in England and Wales who presented in 2020. Results Most patients (88.2%) were not taking any AOM when they presented with hip fracture. Half of all patients (49.9%) were prescribed AOM treatment by the time of discharge, but the proportion deemed ‘inappropriate for AOM’ varied hugely (0.2–83.6%) in different hospitals. Nearly two thirds (64%) of those who were previously taking an oral bisphosphonate were simply discharged on the same type of medication. The total number of patients started on oral medication fell by 11.4% over 5 years. The number started on injectable AOM almost doubled to 14.4% over the same period, but remains hugely variable across the country, with rates ranging 0–67% across different units. Conclusion A recent hip fracture is a strong risk factor for future fractures. If teams are to learn from each other’s experience and patients are to be protected against further fragility fractures the huge variability in approaches, and in particular to the use of injectables, in different trauma units across England and Wales requires further investigation.
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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 | 0.001 |
| 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 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".