Bi-Layer Wound Dressing System for Combat Casualty Care
Bibliographic record
Abstract
Burn injuries remain a significant cause of morbidity and mortality during modern military conflicts and peacekeeping operations. Considering that commercially available dressings are not designed to meet the challenges of treating combat burn wounds, DRDC-Toronto has designed a novel, absorbent and medicated bi-layer wound dressing to address key requirements for treating external war wounds. In the present report, we assess our dressing's bactericidal efficacy, wound healing properties, and skin-cooling characteristics using various pre-clinical models. Biopsies taken from full-thickness, pig wounds infected with Ps. aeruginosa and Staph. epidermidis showed a 2- to 5-log reduction in the bacterial load of antiseptic-treated wounds compared to those of control wounds. Though increasing the frequency of dressing changes led to a greater reduction in the wound bacterial load, the contamination levels of all antiseptic-treated wounds remained below 105 CFU/g of wound. Our results also show that 97% of partial-thickness, non-contaminated porcine wounds treated with the DRDC dressing healed within 7 days. In contrast, 92% of the wounds treated with commercial dressings healed within 9 days.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".