A Single Tertiary-Care Center Case Series Using Vertical Rectus Abdominis Myocutaneous Flap in the Management of Complex Periprosthetic Joint Infection of the Hip
Bibliographic record
Abstract
Prosthetic joint infections (PJIs) pose significant challenges, often requiring multiple surgeries that lead to soft tissue loss, dead space, and fibrosis. Wound breakdown increases the risk of polymicrobial infection and treatment failure. The vertical rectus abdominis myocutaneous (VRAM) flap is a proven method for complex wound coverage, but its role in managing hip PJI is underexplored. This study evaluates outcomes of VRAM flap reconstruction in polymicrobial hip PJI. We retrospectively reviewed five patients who underwent VRAM flap reconstruction for polymicrobial hip PJI between December 2020 and December 2023. Primary outcomes included flap survival, infection control, and wound healing. Secondary outcomes were implant retention, postoperative complications, and functional status. At a mean follow-up of 28 months, four patients achieved wound healing and remained infection-free, while one had persistent sinus drainage but retained the implant. Flap survival was 100%, with no necrosis or failure. No major complications requiring reoperation occurred. Two patients developed deep collections, managed with ultrasound-guided drainage (Clavien-Dindo IIIa). Minor complications included donor-site dehiscence (three), flap dehiscence (one), edge necrosis (two), and hernias (two), all managed non-surgically (Clavien-Dindo I/II). All patients retained implants and remained ambulatory. VRAM flap reconstruction is a reliable option for managing complex polymicrobial hip PJI. Flap survival was excellent, and most patients achieved infection control. However, persistent infection and the need for suppressive antibiotics highlight the ongoing challenges in these cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".