Postoperative Load Bearing in Periprosthetic Femoral Fractures Around Hip Arthroplasty: A Survey Among Orthopedic Surgeons in the Netherlands
Bibliographic record
Abstract
INTRODUCTION: Permissive weight bearing (PWB) has relatively recently been implemented to optimize rapid clinical recovery and restoration of function in patients suffering lower extremity fractures. PWB shows outcome advantages in this patient category. Currently, there are no decisive recommendations on postoperative load-bearing management after surgically treated periprosthetic femoral fractures (PPFF) around hip arthroplasty. The objective is to investigate the current postoperative practice of weight-bearing instructions for patients with surgically treated PPFF, accounting for differences in types of periprosthetic fractures and treatment options among Dutch orthopedic surgeons. MATERIALS AND METHODS: An online survey was distributed among the members of the hip and trauma working groups of the Dutch Orthopedic Association. RESULTS: The response rate was 13% (n=75). The main finding was that postoperative load bearing regimes in Vancouver A, B, and C PPFFs differed greatly among Dutch orthopedic surgeons, and there was no decisive guideline or consensus in postoperative load bearing regimes after surgically treated PPFF was used in the Netherlands. CONCLUSION: In the absence of decisive guidelines or consensus, more research is needed to explore the efficacy of PWB after surgically treated PPFF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".