Risk Factors and Predictors of 1-Year Mortality in 262 Vancouver Type C Periprosthetic Femoral Fractures: Insights from the PIPPAS Prospective Multicenter Observational Study
Bibliographic record
Abstract
Background/Objectives: Vancouver type C periprosthetic femoral fractures (VC-PFFs) predominantly affect frail elderly patients and are associated with high mortality, yet limited evidence exists regarding prognostic factors. The PIPPAS study (Peri-Implant and PeriProsthetic Survival Analysis) sub-analysis aimed to investigate the risk factors for one-year mortality following VC-PFF and identify predictors of medical and surgical complications. Methods: This prospective, multicenter, observational case series was conducted across 59 hospitals in Spain and involved 262 VC-PFF patients between January 2021 and April 2023 with a minimum 1-year follow-up. Demographic, clinical, management, and surgical and outcome data were collected. Logistic regression models were used to identify predictors of one-year mortality and complications. Results: One-year mortality was 30.1%. VC-PFF patients were elderly (median age 85 years, IQR (12.75)), female (77.1%) and frail: median clinical frailty scale 5, IQR (2), mild cognitive impairment (median Pfeiffer score 3, IQR (5)), and multiple comorbidities (median age-adjusted Charlson comorbidity index (a-CCI) 6, IQR (2)). Surgery was performed in 94.7% of cases, primarily with plate osteosynthesis (62.3%) or intramedullary nailing (29.1%). Male sex, higher age, frailty, cognitive impairment, ASA score, and a-CCI were significantly associated with increased mortality. Protective factors included higher hemoglobin levels, surgical treatment, and early postoperative ambulation. No significant difference in mortality was observed between fixation techniques. Conclusions: One-year mortality in VC-PFF patients is high. These findings underscore the need for individualized treatment plans and reinforce the role of early co-management and clinical optimization.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".