Sutures versus staples for wound closure in orthopaedic surgery: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: A recently published meta-analysis comparing metallic staples to sutures in orthopaedic procedures revealed three fold increase in risk for infection in stapled wounds. The studies included in the meta-analysis are at risk of bias due to experimental design limitations. A large randomized controlled trial is proposed to direct orthopaedic surgeons in their choice of wound closure material. METHODS/DESIGN: A parallel group randomized controlled trial with institutional review board approval will be conducted. Patients will be randomized intraoperatively to have skin wounds closed with sutures or staples. Dressings will be used to maintain blinding outcome assessors. The primary outcome measure will be a composite all-cause wound complication outcome measure composed of: infection, wound drainage, wound necrosis, blistering, dehiscence, suture abscess and material sensitivity reaction. An independent review board blinded to treatment assignment will adjudicate suspected complications based on clinical data. All deceased patients will also be reviewed. An interim analysis of complications will take place after half of the patients have been recruited. All data will be analyzed by a blinded statistician. Dichotomous primary and secondary outcome measures will be analyzed using the Chi-squared statistic. Continuous outcome measures will be analyzed using Student's t-test. Subgroup analysis will compare infection rates using sutures versus staples in each anatomic area (upper extremity, pelvis/acetabulum, hip/femur, knee, ankle). A further subgroup analysis will be conducted comparing trauma patients to elective surgery patients. Non-infected revision surgery will also be compared to primary surgery. DISCUSSION: Wound closure material is an afterthought for many orthopaedic surgeons. The combined results of several comparative trials suggests that the choice of wound closure materials may have an impact on the rate of surgical site infections. However, the strength of the evidence is poor given the heterogeneity of the methods employed in previous studies. The following study protocol aims to guide surgeons in their choice of wound closure material by determining if there is a difference in complication rates in sutured and stapled wounds. TRIAL REGISTRATION: This trial was registered at ClinicalTrials.gov under the identifier NCT01146236 (registered June 14, 2010).
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".