Sutures versus staples for wound closure in orthopaedic surgery: a pilot randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: In the spectrum of surgical decision-making, wound closure material is often an afterthought. However, the findings of a recent meta-analysis suggest that the rate of surgical site infections (SSIs) is increased by using staples to close surgical wounds. Less clear is the effect of closure material on the incidence of non-infectious wound complications.The aim of this study was to compare sutures and staples in terms of: incidence of wound complications to determine the sample size for a definitive trial comparing wound closure methods. METHODS: Eligible adult orthopaedic patients were randomized to have wounds closed with sutures or staples. Time for skin closure was recorded. Wounds were assessed for complications for six weeks. The incidence of complications was compared using Fisher's exact test. Time to close and pain with removal of closure material were compared using a Student's t-test. RESULTS: The total number of patients reporting a wound complication was 59 of 148 patients completing six-week followup (41%), with no differennce between sutures and staples (RR = 0.77, CI = 0.52-1.14). The time to close wounds was shorter in the staple group (mean=4.8 min, CI = 2.6-7.1) than the suture group (mean=12 min, CI = 7.9-16). Patients in the staple group (mean=3.7, CI =2.8-4.6) reported more pain with removal than suture group (mean=2.5, CI =1.6-3.4). CONCLUSIONS: This study suggests that 42% of patients report a wound complication with no difference between sutures and staples. It was demonstrated that suturing skin requires more time and staples are more painful to remove. TRIAL REGISTRATION: Clinicaltrials.gov 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".