Duration of cooling with water for thermal burns as a first aid intervention: A systematic review
Bibliographic record
Abstract
BACKGROUND: Cooling thermal burns with running water is a recommended first aid intervention. However, guidance on the ideal duration of cooling remains controversial and inconsistent across organisations. AIM: To perform a systematic review of the evidence for the question; Among adults and children with thermal burn, does active cooling using running water as an immediate first aid intervention for 20 min or more, compared with active cooling using running water for any other duration, change the outcomes of burn size, burn depth, pain, adverse outcome (hypothermia) or complications? METHOD: We searched Medline, Embase, Cochrane Database of Systematic Reviews and used ROBINS-I to assess for risk of bias. We used Grading of Recommendations, Assessment, Development and Evaluation methodology for determining the certainty of evidence. We included all studies that compared the selected outcomes of the duration of cooling of thermal burns with water in all patient ages. (PROSPERO registration number: CRD42021180665). From 560 screened references, we included four observational studies. In these studies, 48% of burns were cooled for 20 min or more. We found no benefit for a duration of 20 min or more of cooling when compared with less than 20 min of cooling for the outcomes of size and depth of burn, re-epithelialization, or skin grafting. The evidence is of very low certainty owing to limitations in study design, risk of bias and indirectness. CONCLUSION: The optimal duration of cooling for thermal burns remains unknown and future prospective research is indicated to better define this treatment recommendation.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".