Neck of femur fracture management by general surgeons at a rural hospital
Bibliographic record
Abstract
BACKGROUND: Neck of femur (NOF) fractures are the most common injury among elderly patients and a significant burden on our healthcare system. AIMS: This study aimed toevaluate if an Australian rural hospital serviced by general surgeons can meet the established standards of care for the management of NOF fractures by undertaking surgery within 48 hours. METHODS: An audit of patients presenting to an Australian rural hospital with NOF fractures over a seven-year period. Patients were excluded if they were transferred or suffered peri-prosthetic or multi-trauma-related fractures. Outcomes included time to surgery, length of stay, and in-hospital mortality, and were compared to three similar Australian studies from hospitals with specialist orthopedic units. Descriptive statistics and meta-analysis were performed. RESULTS: Overall, 182 patients presented with NOF fractures and 114 met our inclusion criteria. Only 12 per cent of patients were transferred. Patients were mostly female (74 per cent) and elderly (mean age 84.0 years). A total of 79 per cent of patients were operated on within48 hours; other studies reported 67-86 per cent. Mean length of stay was 11.9 days (versus 7.7-13.7), and in-hospital mortality was 4 per cent (versus 2-7 per cent). CONCLUSION: This audit suggests that an Australian rural hospital serviced by general surgeons can meet the established standards of care for management of most NOF fractures. Some post-surgery outcomes are similar to those reported by larger centers with specialized orthopedics units.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".