Wound healing in older adults with severe burns: Clinical treatment considerations and challenges
Bibliographic record
Abstract
Background: The older adult population continues to rapidly expand in number, with a projection by the United States (US) Census Bureau that there will be more individuals older than > 65 years (77.0 million) than those younger than < 18 years (76.5 million) by 2034. This review provides an overview of aging as it relates to wound healing and burn injuries in older adult patients, summarizes current treatment practices, and addresses the key challenges and considerations for treating severe burn injuries in this specific patient population. Materials and methods: A narrative literature search was conducted, focusing on recent primary literature on burns and wound healing in elderly patients. Results: Studies showed that the aging process results in both physiologic (eg, nutritional and metabolic status) and anatomic changes (eg, thinning dermis) that contribute to a reduced capacity to recover from burn-injury trauma compared with younger patients. Owing to impaired vision, decreased coordination, comorbidities, and medication-induced side effects, older adults (ie, > 65 years) are susceptible to severe burn injury (deep-partial thickness and full-thickness), which is associated with significant morbidity and mortality. Conclusion: A better understanding of the effects of age-related changes regarding wound healing in older adult patients who incur severe burn injuries may provide insight into clinical strategies to improve outcomes among this population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".