Bacteriology, Inflammation, and Healing
Bibliographic record
Abstract
BACKGROUND: Healing of venous leg ulcers (VLUs) is often stalled despite compression therapy. Increased bacterial burden and chronic inflammation are 2 factors that may prevent these chronic VLUs (CVLUs) from healing. There is evidence that nanocrystalline silver dressings may reduce bacterial levels, decrease the chronic inflammatory response, and thus promote wound healing. OBJECTIVE: To determine the effects of a nanocrystalline silver barrier dressing on wound microflora, wound inflammation, and healing in CVLUs. METHOD: Stalled VLUs in 15 patients were managed using nanocrystalline silver dressings under 4-layer compression bandages. Paired skin biopsies at baseline and at an average of study week 6.5 were analyzed for bacteria and inflammatory infiltrates. Serum silver levels were monitored, and wound healing was assessed using planimetry. RESULTS: VLUs in 4 patients healed, and 8 other patients completed the 12-week study. There was a significant reduction in the log10 total bacterial count between baseline and final biopsies (P = .011). Greater numbers of lymphocytes were associated with an increased reduction of ulcer size at week 6.5 and final assessment at week 12 (P < .05). Heavy neutrophilic infiltration in skin biopsies at week 6.5 was associated with high bacterial counts and delayed healing (P = .037). The median reduction in ulcer surface area for all patients was 83.5%. Serum silver levels increased slightly, but values were within the normal range. CONCLUSION: A nanocrystalline silver dressing combined with 4-layer bandaging was safe and successful in promoting healing in stalled CVLUs. Healing was associated with a reduction in wound bacteria and neutrophilic inflammation with an associated persistent or high lymphocyte count, as determined by wound biopsy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".