[Effectiveness of stepwise progressive ultra-tension-reducing suture method in treatment of high-tension wounds on chest, back, and limbs].
Bibliographic record
Abstract
Objective: To investigate effectiveness of a novel suture method-stepwise progressive ultra-tension-reducing suture method in closing high-tension wounds on the chest, back, and limbs. Methods: A retrospective analysis was conducted on 25 patients with high-tension wounds on the chest, back, and limbs who were treated with stepwise progressive ultra-tension-reducing suture method between January 2022 and December 2022. Among the patients, there were 8 males and 17 females, with an average age of 30.5 years (range, 18-56 years). All wounds after scar or tumor resection were located on the chest, back, upper limbs, and lower limbs in 8, 6, 9, and 2 cases, respectively. The size of wounds ranged from 3.5 cm×2.8 cm to 40.0 cm×15.0 cm. All patients were advised to use topical silicone-based treatments postoperatively. The protrusion of the incision, the height of the protrusion, and the duration of the tension reduction effect were observed. The scar formation at the incision site at 6 months after operation was observed, the scar appearance was evaluated by Vancouver Scar Scale (VSS) score, and the scar width was measured. The patient's satisfaction and adverse reactions to incisions were also evaluated. Results: The incisions significantly elevated, with a height of 0.3-2.5 cm, and the tension reducing effect lasted for 8.5-18.0 weeks after operation, with an average of 13.6 weeks. All incisons healed by first intention. One patient experienced transient hyperpigmentation, which resolved spontaneously. Three keloid patients showed localized redness postoperatively, and 2 experienced local recurrence, which improved significantly after treatment with triamcinolone, 5-fluorouracil injections, and laser therapy. All patients were followed up 6.0-13.5 months, with an average of 10.1 months. At 6 months after operation, all patients had linear scars, with VSS scores ranging from 1.0 to 3.5 (mean, 2.0). The width of the scars ranged from 0.5 to 3.0 mm (mean, 1.4 mm). The patients expressed satisfaction with the effectiveness. Conclusion: The stepwise progressive ultra-tension-reducing suture method for high-tension wounds can effectively reduce the tension at the wound edges, providing a prolonged tension-reducing effect and satisfactory effectiveness.
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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 | 0.000 | 0.001 |
| 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".