Mortality Rate in Periprosthetic Proximal Femoral Fractures: Impact of Time to Surgery
Bibliographic record
Abstract
Hip replacement surgery is increasingly being performed on older patients, raising the risk of periprosthetic proximal femur fractures (PPFFs). While the impact of surgery timing on mortality in proximal femoral fractures is established, its effect on PPFFs remains unclear. This study aims to examine the correlation between surgery timing and mortality in PPFF patients. In a historical cohort study, we analyzed data from 79 PPFF patients treated from 2012 to 2022. Patients were categorized by surgery timing (≤48 h, 32 patients vs. >48 h, 47 patients). Outcomes and mortality rates were compared. No significant difference in mortality was observed between patients undergoing early (<48 h) and delayed (>48 h) surgery at 30 days and 1 year. Factors such as age (p = 0.154), gender (p = 0.058), ASA score (p = 0.893), Vancouver classification (p = 0.577), and surgery type (implant revision p = 0.691, OR = 0.667) did not affect 30-day mortality. However, 1-year mortality was influenced by gender (male p = 0.045) and age (p = 0.004), but not by other variables (Vancouver classification p = 0.443, implant revision p = 0.196). These findings indicate no association between surgery timing and mortality in PPFF patients, suggesting that other factors may influence outcomes. Further research is needed to optimize PPFF management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".