Evaluating antimicrobial efficacy of new commercially available silver dressings
Bibliographic record
Abstract
Prevention and treatment of bacterial colonised/infected wounds are critical. Many commercially available silver dressings claim broad-spectrum bactericidal activity over days and are indicated for serious conditions including burns and ulcers. However, there is no peer-reviewed literature available for many newer dressings. This study compared the activity of some of these dressings. Six silver-containing dressings were compared using log reduction, silver release and corrected zone of inhibition assays. Only the nanocrystalline silver dressing was bactericidal against Staphylococcus aureus, and the only other dressing that produced any log reduction was a silver collagen matrix dressing. These two dressings and a silver alginate dressing produced zones of inhibition, although the collagen matrix and alginate dressings had decreasing zone sizes over time, and the latter liquefied after five transfers. The remaining dressings (two ionic silver foam dressings and a silver sulphate dressing) did not produce zones of inhibition. For the foam, alginate and collagen matrix dressings, antimicrobial activity was related to silver release. The silver sulphate dressing released large quantities of silver, but only through the dressing edges, as the wound-contacting surface appeared to be hydrophobic. The results of this study emphasise the importance of confirming product claims regarding silver dressing efficacy.
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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) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".