Redefining the concept of the elderly burn patient: Analysis of a multicentre international dataset
Bibliographic record
Abstract
BACKGROUND: The elderly are highly vulnerable to major burn injuries. Typically, 'elderly' is accepted as ≥ 65 years of age. This cut-off is arbitrary, lacks a robust evidence base and is potentially damaging from a clinical-decision-making perspective. The study objective was to utilise a large international dataset of major burns to stratify mortality risk by age and objectively define 'elderly' patients with significantly higher risk of poor outcome. METHODS: We performed a sub-analysis of the RE-ENERGIZE clinical trial dataset. RE-ENERGIZE included 1200 patients admitted to 54 burn centres worldwide with 2nd and/or 3rd degree burns, who were expected to require skin grafting. In a first-of-its-kind age stratification study, we stratified major burns patients by five-year age intervals. Logistic regression and Cox proportional hazards analyses were performed with three-month mortality and time-to-discharge-alive (TTDA) as the primary and secondary outcomes. RESULTS: Three-month mortality was 15.41 %. Age was associated with three-month mortality upon multivariable logistic regression analysis (p = 0.000, OR=1.06, CI=1.05-1.08), independently of total burn surface area burned (TBSA%), Acute Physiology and Chronic Health Evaluation II (APACHE II) and Charlson Comorbidity Index (CCI). Age 80 + was independently associated with increased mortality and TTDA, when compared to all referent 5-year age groups (p ≤ 0.000-0.043). The Lethal Dose 50 (LD50) for the 80 + group was 20.5 %. CONCLUSIONS: We present a new threshold of risk stratification in patients with major burns; Patients ≥ 80 years have a significantly poorer outcome, irrespective of injury severity, resultant critical illness severity, and variables including comorbidities, which has implications for prognostication and management decisions.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".