Silver-coated nylon dressings for pediatric burn victims
Bibliographic record
Abstract
BACKGROUND: Silver dressings are a proven method for burn treatment.Current challenges associated with burn treatment include pain management and limited hospital resources.A new silver-coated nylon dressing was used at the Montreal Children's Hospital (Montreal, Quebec) to help reduce traumatic dressing changes and cost.METHODS: Burn victims in a pediatric patient population were followed over two years.Patients were excluded if they were evaluated more than 48 h postburn or if the burn affected less than 5% of the total body surface area.The same burn team admitted and treated all case subjects, and one dressing nurse recorded and monitored all progress throughout the study to ensure standardization.RESULTS: Fifteen patients were included in the study.The average number of dressing changes needed was 4.13, with a median of three changes.The average total body surface area burned was 8%, with a mean of 13.9 days before superficial wounds were re-epithelialized.The average length of in-hospital stay was four days.The cost was $388 less for silver-coated nylon dressings than for silver sulfadiazine cream for seven days of treatment.Silver-coated nylon dressings did not leave any residue or pseudoeschar on the wounds and were easily maintained at home. CONCLUSION:The silver-coated nylon dressings are as effective as other silver dressings used for pediatric burn victims.The dressings are less traumatic, require fewer resources and do not leave wound residue compared with other dressings.
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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) | 0.003 | 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".