Outcome Evaluation of Burn Injury Management: A Study of Selective Traditional Home Remedies
Bibliographic record
Abstract
Background Clinicians classify burns as epidermal, partial thickness (superficial and deep), or full thickness, according to the depth of tissue damage. Although skin is considered the largest organ in the human body, studies investigating burns, their types, and their management has revealed that the background knowledge of burn aid the community possesses remains unsatisfactory. Thus, in this study, we aimed to evaluate the effect of various traditional home remedies, taking into account the type of burns and the nature of the remedies used from a cosmetic point of view. Materials and methods This is an original retrospective study conducted at Dr. Soliman Fakeeh Hospital in Jeddah from June through December 2022. Using the Vancouver Scar Scale (VSS), eligible patients who met our inclusion criteria were invited to participate in the study after a review of their patient history, an assessment of basic vital signs, and a physical examination. Results Fifty-two participants met our inclusion criteria and successfully completed the study. A total of 80 wounds of varying severity in various locations were evaluated. Participants were divided into three categories according to VSS scores indicating good, intermediate, or poor healing. None of the eight cases treated with water resulted in poor healing. However, tomato paste resulted in poor healing for six cases (60%) but moderate and good healing for two cases (20%). Conclusion The safest and most effective initial management for burns among all the reviewed remedies was the application of cool running water, followed by seeking medical attention for evaluation and proper treatment, whereas tomato paste had a markedly poor effect.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".