Split‐level folding, step‐type tension‐relieving suture technique, and the evaluation on scar minimization
Bibliographic record
Abstract
BACKGROUND: Prevailing tension-reducing suture methods have a spectrum of issues. This study presents a straightforward yet highly efficacious suture technique known as the Split-level Folding, Step-type Tension-relieving Suture technique, which could play a pivotal role in preempting incisional scarring. AIMS: To introduce Split-level Folding, Step-type Tension-relieving Suture technique and assess its effect on scar minimization. METHODS: A retrospective analysis of 64 patients who underwent treatment utilizing the proposed suturing methodology. Assessment parameters included the Patient and Observer Scar Assessment Scale (POSAS), the Vancouver Scar Scale (VSS), scar width, complications, and all evaluated at 6- and 12-month postoperatively. RESULTS: At 12-month follow-up, the POSAS and VSS scores in the normal suture group (32.58 ± 5.43, 3.58 ± 1.39) were considerably higher than the step-type suture group (29.75 ± 3.56, p = 0.0007; 2.78 ± 1.17, p = 0.0006). Moreover, the step-type suture group showcased a significantly narrower average incision scar width (1.62 ± 0.36) than the normal suture group (1.87 ± 0.42, p = 0.0004). This novel tension-relieving suture technique that effectively circumvents the occurrence of persistent localized eversion and other complications often associated with traditional tension-relieving sutures. CONCLUSIONS: The Split-level Folding, Step-type Tension-relieving Suture technique emerges as a highly promising option for averting incisional scarring. This suture method works well for incisions on the chest, back, and extremities, resulting in significantly better long-term outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".