Outcomes of Treatment of Periprosthetic Femoral Fractures after Total Hip Replacement – Experience of Department of Orthopedics and Traumatology, Medical University of Warsaw
Bibliographic record
Abstract
BACKGROUND: As more and more hip replacement procedures are being performed, the incidence of periprosthetic femoral fractures is also growing. The aim of the study was to assess the outcomes of periprosthetic fracture treatment with the use of the functional Harris Hip Score (HHS). It compares the outcomes of periprosthetic fracture treatment and prefracture treatment results obtained during follow-up outpatient consultations. MATERIAL AND METHODS: The study involved 65 patients divided into the following four groups, depending on the type of the primary procedure: patients with periprosthetic femoral fractures after hip hemiarthroplasty; patients after cemented total hip replacement; patients after cementless arthroplasty; and patients after revision hip arthroplasty. The types of fractures were classified according to the Vancouver scale. RESULTS: The HHS questionnaires showed a deterioration of treatment outcomes in all patients from the four groups as compared with the outcomes obtained prior to the fracture. The most pronounced decrease in functional outcomes was observed in the group of patients after hemiarthroplasty and revision hip replacement. CONCLUSIONS: 1. The outcomes of periprosthetic fracture treatment, assessed with the HHS, are worse than the results obtained prior to the fracture. 2. Good results of periprosthetic fracture treatment were obtained only in patients with Vancouver Type B1 fractures. 3. The presence of co-morbidities in elderly patients contributes to a poorer final result of periprosthetic fracture treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".