Prospective Clinical Study to Assess the Novel Technique of Collagen Application Over Meshed Split Thickness Graft for Wound Coverage
Bibliographic record
Abstract
Aim: A Novel Technique of Collagen Application over Meshed Split Thickness Graft for Wound Coverage. Methods: This prospective study conducted in the Department of surgery, Anugrah Narayan Magadh Medical College and Hospital, Gaya, Bihar, India for 1 year. All cases with a raw area of 5-20% of total body surface area with the need for STSG for wound coverage, irrespective of the sex of patients, were included in the study. Children < 10 yrs and adults > 70 yrs were not part of the study. Results: Causes for wounding requiring STSG included trauma (8 cases), burns (5 cases) and its sequelae contracture (4 cases), diabetic ulcer foot (3 cases) and a case of Meleney’s gangrene. The lower extremity (10 cases) was the most common area requiring skin grafting in this study, followed by the trunk (7 cases) and upper extremity (3 cases). A total of 10 patients had co-morbidities. 2 patients were on treatment for diabetes mellitus, hypertension and congestive heart disease. Out of the other 8 patients, 5 typed II DM on oral hypoglycemic, 2 were on anti-hypertensives, and 1 was on treatment for hypothyroidism. All patients were adequately prepared for surgery. The majority of the patients were discharged after 2nd dressing between 5-11 days. Characteristics of grafted area: Vancouver scar scale (VSS) was used to determine the outcome of the grafted area. A score of more than 4 was considered a hypertrophic scar. The mean score of 20 patients at the end of 2 weeks, 1, 2, 4 and 6 months was 0.13, 0.25, 0.54, 1.07 and 1.48. Since the scoring used to determine the outcome of this technique did not take into account patient satisfaction, the same was individually determined. Conclusion: As observed in the results, this technique has produced a very favourable outcome. However, it requires evaluation of procedure in a large cohort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".