Outcomes of Abductor Repair Using Mesh Augmentation in Oncologic Proximal Femur Replacement
Bibliographic record
Abstract
Reconstruction of the abductor mechanism remains a primary challenge with contemporary proximal femoral replacement (PFR) surgery. Previously, techniques such as trochanteric preservation or direct repair to the implant have been described; however, these strategies are limited in their ability to tension the repair and reattach other muscles of the hip girdle. The aim of this study was to evaluate the outcomes of patients undergoing oncologic PFR using a novel technique of mesh augmentation for soft tissue repair. METHODS: We reviewed 18 (mean age 64 years; 8 female: 10 male) consecutive patients undergoing PFR with Marlex mesh augmentation between 2018 and 2023 at a single institution. The most common indication was metastatic disease (n = 13). The mean follow-up in the 14 surviving patients was 27 months (range 12-34). RESULTS: All patients were ambulatory at final follow-up. There were no post-operative dislocations, infections, or wound issues. At the final follow-up, the mean total MSTS score was 77%. CONCLUSION: Mesh augmentation of PFRs allowed for adequate soft tissue tensioning and muscular attachment to the body of the implant. In our series, this technique was durable, with no dislocations and no mesh-related complications. In summary, mesh augmentation of PFRs may be considered during reconstruction for oncologic indications.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".