A prospective comparative evaluation of wound healing and scar formation using surgical sutures, surgical staplers, and tissue glue
Bibliographic record
Abstract
Objectives: Techniques for closing wounds have progressed from early developments in suturing material to advanced resources, including skin staplers, adhesive tapes, and, most recently, cyanoacrylate tissue adhesives. Yet, no gold standard technique promises an absolute skin flap approximation with obliteration of underlying space to promote quick recovery and optimal esthetic results with minimized complications. Materials and Methods: Our study compared the effectiveness of tissue glue, surgical staplers, and sutures. This careful design included 225 patients randomly allocated into three groups of 75 patients each: Group(A) – tissue glue, Group(B) – skin staplers, and Group(C) – sutures. We evaluated wound infection using the Additional treatment, Serous discharge, Erythema, Purulent exudate, Separation of deep tissues, Isolation of bacteria, and duration of inpatient stay (ASEPSIS) score, pain levels using the visual analog scale, and scar formation using the Vancouver scale. The final analysis used patient satisfaction (PS) scores and the surgeon’s satisfaction scores. Results: The mean asepsis score was found to be lowest for Group A, with values of 12.28 (7 th day), 9.88 (14 th day), 6.48 (30 th day), and 3.92 (60 th day). However, no significant difference was found between groups in wound infection rates. The pain was higher on the 14 th day of the procedure in Group B and Group C patients. There was a significant difference between the groups, comparing the Vancouver Scale, with glue showing a better cosmetic outcome. Conclusion: Our study established that tissue glue, with its faster wound closure, lower pain levels, and patient and doctor convenience, emerged as a promising option. These findings have significant practical implications, as they can potentially improve patient outcomes, reduce healthcare costs, and instill a sense of optimism about their application in wound closure techniques.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".