The Effectiveness of Intradermal Suture Technique on Hypertrophic Scar Prevention in Rats
Bibliographic record
Abstract
Introduction : Hypertrophic scarring is a complication that occurs in post-surgical wounds. There are many ways to prevent scarring, but there have been no satisfactory methods yet. Moreover, no studies investigated the effectiveness of intradermal sutures. Polypropylene thread can be used to repair a scar after surgery. This study aimed to prove the effectiveness of intradermal sutures using polypropylene thread on surgical scar quality.Method : The study was conducted using a randomized, controlled trial, and post-test only with 20 rats (Rattus norvegicus) as animal subjects. The wounds were made on the back, approximately 6 mm x 2 cm. Then, the rats were divided into two groups: a control group without intradermal sutures (K) and treatment group with intradermal sutures (I). Histopathological examination using Hematoxylin and Eosin (H&E) staining was used to identify the fibroblast number on the 21st day. The fibrocytes number were also observed on the 12th week and the Vancouver Scar Scale (VSS) was used to assess the scar quality.Result : The number of fibroblasts and fibrocytes in the control group (K) was significantly smaller (53.60 ± 14.571; 3.20 ± 0.447) than the treatment (I) (243.20 ± 75.334; 171.40 ± 13.221). The VSS value was significantly greater in the control than the treatment.Conclusion: The intradermal sutures using polypropylene thread produced a better-quality scar after surgery compared to wounds without intradermal sutures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".