Pathophysiologic Response to Burns in the Elderly
Bibliographic record
Abstract
Over the last decades advancements have improved survival and outcomes of severely burned patients except one population, elderly. The Lethal Dose 50 (LD50) burn size in elderly has remained the same over the past three decades, and so has morbidity and mortality, despite the increased demand for elderly burn care. The objective of this study is to gain insights on why elderly burn patients have had such a poor outcome when compared to adult burn patients. The significance of this project is that to this date, burn care providers recognize the extreme poor outcome of elderly, but the reason remains unclear. In this prospective translational trial, we have determined clinical, metabolic, inflammatory, immune, and skin healing aspects. We found that elderly have a profound increased mortality, more premorbid conditions, and stay at the hospital for longer, p < 0.05. Interestingly, we could not find a higher incidence of infection or sepsis in elderly, p > 0.05, but a significant increased incidence of multi organ failure, p < 0.05. These clinical outcomes were associated with a delayed hypermetabolic response, increased hyperglycemic and hyperlipidemic responses, inversed inflammatory response, immune-compromisation and substantial delay in wound healing predominantly due to alteration in characteristics of progenitor cells, p < 0.05. In summary, elderly have substantially different responses to burns when compared to adults associated with increased morbidity and mortality. This study indicates that these responses are complex and not linear, requiring a multi-modal approach to improve the outcome of severely burned elderly.
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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 | 0.001 |
| 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) | 0.002 | 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".