POSTOPERATIVE PERIPROSTHETIC FRACTURES IN HIP REPLACEMENT
Bibliographic record
Abstract
Introduction: The incidence of postoperative periprosthetic femoral fractures ranges from 0.1% in primary arthroplasty to 2.1% in revision surgery, and is often a challenge for the surgeon. Materials and methods: We carried out a retrospective clinical study of periprosthetic femoral fractures found among the primary arthroplasties and revision hip replacements performed at San Carlos University Hospital between 1991 and 2003. We found 82 patients with postoperative periprosthetic femoral fractures. The fractures were classified according to the Vancouver classification, and we analysed the associated risk factors, treatments used, complications and results. Results: The mean age of the patients was 72 (SD 12). There were 57 women (69.5%) and 25 men (30.5%). Of the 82 cases, 22 (26.8%) were type B1 fractures, 33 type B2 (40.2%), 20 type B3 (24.4%) and 7 type C (8.5%). The most common surgical treatment was the combination of a long stem held in place with cerclage wires in 27 cases (33%), followed by treatments using allografts in different combinations in 22 cases (26.8%). Conclusions: Femoral bone stock is a factor that influences the occurrence of periprosthetic fractures. The use of allografts has little effect on the fracture consolidation time, although it involves an increase of femoral bone stock, which makes allografts advisable even in Vancouver type B2 fractures.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".