Negative pressure wound therapy for high-risk wounds in lower extremity revascularization: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Rates of surgical site infections (SSIs) following groin incision for femoral artery exposure are much higher than expected of a clean operation. The morbidity and mortality is high, particularly with the use of prosthetic grafts. The vascular surgery population is at an increased risk of SSIs related to peripheral vascular disease (PVD), diabetes, obesity, previous surgery and presence of tissue loss. Negative pressure wound therapy (NPWT) dressings have been used on primarily closed incisions to reduce surgical site infections in other surgical disciplines. We have not come across any randomized controlled trials to support the prophylactic use of negative pressure wound therapy in high-risk vascular patients undergoing lower limb revascularization. METHODS/DESIGN: In this single-center, prospective randomized controlled trial, patients scheduled for a lower limb revascularization requiring open femoral artery exposure who are at a high risk (BMI > 30 kg/m(2), previous femoral cutdown or Rutherford V or VI category for chronic limb ischemia) will be eligible for the study. A total of 108 groin incisions will be randomized to the use of a negative pressure wound device or standard adhesive gauze dressing. Patients will be followed in hospital and reassessed within the first 30 days postoperatively. The primary outcome is SSI within the first 30 days of surgery and will be determined using the intention-to-treat principle. Secondary outcomes include length of stay, emergency room visits, reoperation, amputation and mortality. A cost analysis will be performed. DISCUSSION: The trial is expected to define the role of NPWT in SSI prophylaxis for lower limb revascularization in high-risk vascular patients. The results of the study will be used to inform current best practice for perioperative care and the minimization of SSIs. TRIAL REGISTRATION: NCT02084017 , March 2014.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".