Assessment of the Effectiveness of the Novel Technique of Collagen Application Over Meshed Split Thickness Graft for Wound Coverage: A Prospective Study
Bibliographic record
Abstract
Aim: To study the advantages of a novel technique of using collagen sheet over meshed split thickness graft for wound coverage. Methodology: A prospective study was conducted at Department of general surgery at Sheikh Bhikhari Medical College and Hospital, Hazaribagh, Jharkhand, India. A total of 25 patients were part of this study intending to follow each patient at least for a minimum of 6 months postoperatively. All patients underwent relevant routine investigations. Patients were regularly evaluated for postoperative complications and outcomes. Patients were asked to provide their objective pain assessments on a Pain scale from ‘0-10’ at regular intervals. For scar assessment, Vancouver Scar Scale (VSS) was used. Patient’s overall satisfaction was also accounted. Results: Out of 25 patients, 15 (60%) were males and 10 (40%) were females. The majority of patients in the study were in 3rd, 4th and 5th decades. 11 (44%), 8 (32%), and 6 (24%) patients belonged to 3rd, 4th and 5th decade of life respectively. The lower extremity (11, 44%) was the most common area requiring skin grafting, followed by the trunk (9, 36%) and upper extremity (5, 20%) area. The mean VSS score of 25 patients at the end of 1, 2, 4 and 6 months was 0.30, 0.48, 1.04 and 2.17. Out of 25 patients, 1 patient had score more than 4 at the end of 6 months indicating hypertonic scar. Conclusion:With satisfying results obtained in this study, we acknowledge collagen for its ease of use and cost-effectiveness. Furthermore, we consider it effective because of the promotion of epithelialization, reduction of pain and limited complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".