Risk factors for mortality in periprosthetic femur fractures about the hip-a retrospective analysis
Bibliographic record
Abstract
PURPOSE: Fractures around the hip are known to be an indicator for fragility and are associated with high mortality and various complications. A special type of fractures around the hip are periprosthetic femur fractures (PPF) after Total Hip Arthroplasty (THA). The aim of this study was to investigate the mortality rate associated with PPF after THA and to identify risk factors that may increase it. METHODS: Consecutive patients (N = 158) who were treated for a PPF after THA in our university hospital between 2010 and 2020 were identified and mortality was assessed using the residential registry. Univariate (Kaplan-Meier-Estimator) and multivariate (Cox-Regression) statistical analysis was performed to identify risk factors influencing mortality. RESULTS: One-year-mortality rate was 23.4% and 2-year mortality was 29.2%. Mortality was significantly influenced by age, gender, treatment, type of comorbidity and time of surgery (p < 0.05). Surgical treatment during regular working hours (8 to 18 h) reduced mortality by 53.2% compared to surgery on call (OR: 0.468, 95% CI 0.223, 0.986; p = 0.046). For every year of age, mortality risk increased by 12.9% (OR: 1,129, 95% CI 1.078, 1.182; p < 0.001). The type of fracture according to the Vancouver classification had no influence on mortality (p = 0.179). Plate fixation and conservative treatment were associated with a higher mortality compared to revision arthroplasty (plate: OR 2.8, 95% CI 1.318, 5.998; p = 0.007; conservative: OR 2.5, 95% CI 1.421, 4.507; p = 0.002). CONCLUSION: Surgical treatment during regular working hours is associated with lower mortality compared to surgery outside these hours. In this retrospective cohort, time to surgery showed no significant impact on all-cause mortality, and revision arthroplasty was associated with lower mortality than conservative treatment or plate fixation. LEVEL OF EVIDENCE: IV (Retrospective cohort study).
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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 | 0.001 |
| 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 | 0.000 | 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".