Comparing the effect of two types of silver nano-crystalline dressings (acticoat and agcoat) in the treatment of full thickness burn wound.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study was conducted to compare the effect of acticoat and agcoat dressing (2 types of silver nano-crystalline dressings) in the treatment of burn wounds. Infection is one of the most important causes of death in patients with major burn. Despite using different prevention methods, including prophylaxis antibiotics with broad-spectrum antibiotics, no method has been found to prevent this dangerous complication for burn patients. Topical silver sulfadiazine is one of the best topical antibiotics in infection control of burn wounds, and other forms of AG dressings are also useful. Their advantages are slow releasing, further-half-life, less frequent dressing change, and less pain during replacement. MATERIALS AND METHODS: In this study, 30 patients with infected full thickness burn wound were selected. The patients' age range was 18-85 years, with the mean age of 39.7-17.27. Every patient's wound was divided into 2 parts randomly, one part was dressed with agcoat and the other with acticoat. Sampling of the 2 parts was done before dressing and after the third and seventh day of dressing. RESULTS: The positive outcome of the first day culturing before silver dressing was 80% and 76.7% for agcoat and acticoat, respectively. However, on the third day, it decreased to 30% and 33.3%, respectively. On the seventh day, it further decreased to 20% in both groups, and the percentage of bacterial growth reduction was not significant. CONCLUSION: Based on the results of this study, silver agcoat dressing was as effective as acticoat dressing in preventing burn wound infection.
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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".