Combined Surgical and Medical Treatment for Vancouver B1 and C Periprosthetic Femoral Fractures: A Proposal of a Therapeutic Algorithm While Retaining the Original Stable Stem
Bibliographic record
Abstract
INTRODUCTION: There is lack of consensus regarding best operative fixation strategy for periprosthetic femoral fractures (PFFs) around a stable stem. Evidence exists that some patterns of fracture around a stable stem are better treated with revision surgery than with standard fixation. Anyway, a more aggressive surgical procedure together with medical treatment could allow for stem retention, and reduced risk of nonunion/hardware failure, even in these cases. SIGNIFICANCE: This paper is placed in a broader context of lack of studies on the matter, and its aim is to shed some light on the management of PFFs around a stable stem, when peculiar mechanical and biological aspects are present. RESULTS: Based on our casuistry in the treatment of nonunions after PFF successfully treated with original stem retention, and on review of Literature about risk factors for fixation failure, an algorithm is proposed that can guide in choosing the ideal surgical technique even for first-time PFFs with a stable stem, without resorting to revision. Mechanical (major and minor) and biological (local and systemic) factors that may influence fracture healing, leading to nonunion and hardware failure, and subsequent need for re-operation, are considered. The proposed surgical technique consists of rigid fixation with absolute stability (using a plate and structural allograft) plus local biological support (structural allograft and autologous bone marrow concentrate over a platelet-rich plasma-based scaffold) at fracture site. Systemic anabolic treatment (Teriparatide) is also administered in the post-operative period. CONCLUSION: Mechanical factors are not the only issues to be considered when choosing the surgical approach to PFFs over a stable stem. Systemic and local biological conditions should be taken into account, as well. A therapeutic algorithm is proposed, given the prosthetic stem to be stable, considering mechanical and biological criteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".