PERIPROSTHETIC HIP FRACTURES MANAGED BY REVISION ARTHROPLASTY
Bibliographic record
Abstract
Abstract Introduction In general the life expectancy of population is improving. This is causing to increase case load of peri-prosthesis fractures after joint replacements. We present our results of peri-prosthesis fracture around hip managed by revision arthroplasty. Methods A retrospective analysis of 24 consecutive patients of periprosthetic hip fracture treated with a revision arthroplasty at Major Trauma Centre between February 2021 and January 2022. Results 12 male and 12 female patients, average age 78 years. 3 fractures around BHR prosthesis, 2 type A, 15 type B and 3 of type C (Vancouver). The surgery was done in an average 6 days after injury (range 1–14). 6 patients died in follow up, 1 patient contracted infection, 2 developed LLD and 1 patient had multiple dislocations. 6 patients had revision using endo-prosthesis. Advanced age with peri-prosthesis fracture has increased risk of mortality (average age 84.5 years). Conclusion Endo-prosthesis replacement had higher risk of dislocation, infection and mortality. Overall patients do well after a revision arthroplasty for periprosthetic hip fracture. Declaration of Interest (b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".