The burden of second hip fractures: provincial surgical hospitalizations over 15 years
Bibliographic record
Abstract
Background: Second hip fractures account for up to 15% of all hip fractures. We sought to determine if the proportion of hip fracture surgeries for second hip fracture changed over time in terms of patient and fracture characteristics. Methods: We reviewed the records of patients older than 60 years hospitalized for hip fracture surgery between 1990 and 2005 in British Columbia. We studied the proportion of surgeries for second hip fracture among all hip fracture surgeries. Linear regression tested for trends across fiscal years for women and men. Results: We obtained 46 341 patient records. Second hip fracture accounted for 8.3% of hip fracture surgeries. For women the proportion of second hip fracture surgeries increased linearly from 4% to 13% with each age decade (p = 0.001) and across fiscal years (p = 0.002). In men the proportion of second hip fracture surgeries was 5% for each age decade between the ages of 60 and 90 years across fiscal years, increasing to 8% for men older than 90 years across fiscal years (p = 0.20). These sex-specific trends were similar for both pertrochanteric and transcervical fracture types. Conclusion: Second hip fracture surgeries account for an increasing proportion of hip fracture surgeries and may require more health care resources to minimize poorer reported outcomes. Future research should determine whether more health care resources are required to manage these patients and optimize their outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".