Effect of Suture Material Choice on Wound Healing and Complications After Thyroidectomy: A Prospective Study
Bibliographic record
Abstract
Background: Thyroidectomy is a common surgical procedure, and while generally safe, postoperative wound complications remain a significant concern.The choice of suture material-absorbable versus non-absorbable-may influence healing outcomes, cosmetic results, and complication rates, but evidence specific to thyroid surgery is limited, particularly in the Indian context.Methods: This prospective, comparative observational study was conducted at Churachandpur Medical College, Manipur, India, over 24 months.A total of 180 patients undergoing thyroidectomy were enrolled and divided into absorbable (n=90) and nonabsorbable (n=90) groups.Complications, including wound infection, seroma, hematoma, and dehiscence, were documented.Scar quality was assessed using the Vancouver Scar Scale, and patient satisfaction was measured through structured questionnaires.Data were analyzed using chi-square tests, Kaplan-Meier survival analysis, and multivariate logistic regression.Results: Overall complication rate was 14.4%.Wound dehiscence was significantly higher in the absorbable group (7.8% vs 2.2%, p=0.04), while wound infection (6.7% vs 3.3%) and hematoma (2.2% vs 1.1%) were slightly more common but not statistically significant.Scar outcomes favored non-absorbables, with mean Vancouver Scar Scale scores at 6 months of 3.1 vs 4.2 (p=0.03).Patient satisfaction was higher in the non-absorbable group (85% vs 73%).Logistic regression identified absorbable suture use (OR 2.4, p=0.03) and diabetes (OR 2.1, p=0.04) as independent predictors of dehiscence.Conclusion: Non-absorbable sutures were associated with lower wound dehiscence and better scar outcomes without increasing infection or hematoma risk, making them the preferred choice for thyroidectomy closure, particularly in patients with comorbidities.These findings highlight the need for context-specific surgical guidelines in India.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".