Risk factors for failure after treatment of a periprosthetic fracture of the femur
Bibliographic record
Abstract
Periprosthetic fracture of the femur is an uncommon complication after total hip replacement, but appears to be increasing. We undertook a nationwide observational study to determine the risk factors for failure after treatment of these fractures, examining patient- and implant-related factors, the classification of the fractures and the outcome. Between 1979 and 2000, 1049 periprosthetic fractures of the femur were reported to the Swedish National Hip Arthroplasty Register. Of these, 245 had a further operation after failure of their initial management. Data were collected from the Register and hospital records. The material was analysed by the use of Poisson regression models. It was found that the risk of failure of treatment was reduced for Vancouver type B2 injuries (p = 0.0053) if revision of the implant was undertaken (p = 0.0033) or revision and open reduction and internal fixation (p = 0.0039) were performed. Fractures classified as Vancouver type B1 had a significantly higher risk of failure (p = 0.0001). The strongest negative factor was the use of a single plate for fixation (p = 0.001). The most common reasons for failure in this group were loosening of the femoral prosthesis, nonunion and re-fracture. It is probable that many fractures classified as Vancouver type B1 (n = 304), were in reality type B2 fractures with a loose stem which were not recognised. Plate fixation was inadequate in these cases. The difficulty in separating type B1 from type B2 fractures suggests that the prosthesis should be considered as loose until proven otherwise.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".