Impact of periprosthetic femoral fractures on frailty, mobility and outcomes in hip arthroplasty
Bibliographic record
Abstract
AIM: The demand for total hip arthroplasty and hemiarthroplasty is rising, increasing the incidence of periprosthetic femoral fractures. This study aimed to assess clinical outcomes, including mortality, length of stay, and the impact of periprosthetic femoral fractures on mobility and frailty at one-year follow-up. METHODS: A retrospective analysis of prospectively collected data was conducted looking at periprosthetic femoral fractures at a tertiary referral center from 2018 to 2024. The data collected included comorbidities, fracture classification, treatment method, length of stay, and discharge destination. The mortality rates at 30 days and one year were calculated. Mobility and frailty were assessed via the New Mobility Score and Clinical Frailty Scale before fracture and at one year. Statistical analysis included chi-square and Wilcoxon signed rank tests. RESULTS: A total of n = 79 patients met the inclusion criteria (mean age 79.6 ± 9.5 years). There was a preponderance of females (35:44, M: F, p = 0.311). Vancouver B2 was the most common fracture pattern (n = 38). Surgical fixation was performed in n = 58 patients. Mortality rate at 30-day and one-year were 7.5% (n = 6) and 16.4% (n = 12) respectively. The mean Charlson Comorbidity Index was 4.39, with a score greater than 5 associated with higher one-year mortality (p = 0.031). Nursing home residency increased by 16%. The median New Mobility Score decreased from 7 to 5 (p < 0.001). The median Clinical Frailty Scale score increased from 4 to 5 (p < 0.001). CONCLUSION: Periprosthetic femoral fractures affect elderly, comorbid patients and are associated with high mortality. We observed measurable and significant decreases in mobility and frailty. Prompt treatment and early mobilization should be prioritized to improve 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".