Hip Fractures in Long-Term Care: Is the Excess Explained by the Age and Gender Distribution of the Residents?
Bibliographic record
Abstract
Introduction. This study compares hip fracture rates in Long Term Care (LTC) residents with those in the community to determine if their high rate of fracturing reflects the extreme age and predominantly female nature of that population. Methods. Hospital discharge data in London Ontario (population 350,000) and Statistics Canada data were used to correct the hip fracture rate in the LTC setting for age and gender. Results. The risk of hip fracture is 1.8 times greater in LTC than in the community for people of similar age and gender. The rate in women is 1.5 times higher whereas in men it is 4.3 times higher. In the oldest residents, the risk in men exceeds that of women in LTC. Conclusion. The high hip fracture rate in LTC is not just a reflection of the age and predominantly female nature of this population. The oldest men in LTC are a particularly high risk group, deserving more attention.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".