718 Predictors of Lengthened Admission in Elderly Burn Patients, a Secondary Analysis of 529 Cases
Bibliographic record
Abstract
Abstract Introduction Existing research has examined the relationship between the amount of total body surface area (TBSA) burn and length of stay (LOS) for older adults. As a result, the conventional ratio of 1 day LOS/1% TBSA ratio has been updated to 2 days LOS/1% TBSA. In this study, we aim to elucidate patient and injury characteristics that affect our prognostic indicator, leading to a prolonged LOS. Methods This was a secondary analysis of a cohort study of surviving patients admitted to a tertiary adult burn center between January 1, 2006, and June 30, 2021. Older adult patients aged 60 and over were stratified into expected LOS (< 2.0 days/%TBSA) and greater than expected LOS (>2.0 days/%TBSA). Patient demographics, TBSA, inhalation injury, pre-admission co-morbidities, and in-hospital complications were tabulated. Logistic regressions were performed using IBM SPSS Statistics 29 and Stata Statistical Software: Release 18. Results 529 patients with a mean age of 71 years were included for analysis of the older adult population. 266 patients had the expected LOS/TBSA ratio, and 263 exceeded this ratio. Univariable analysis indicated patients with greater age [1.03 (1.01 – 1.05) p = 0.002], hypertension [1.72 (1.22 – 2.42) p = 0.002], diabetes [1.95 (1.30 – 2.93) p = 0.001], respiratory disease [2.22 (1.40 – 3.53) p = 0.001], cardiac disease [1.75 (1.24 – 2.47) p = 0.002], psychiatric illness [1.75 (1.04 – 2.96) p = 0.035], and those who experienced complications (such as infection, graft failure, pneumonia, sepsis) [1.62 (1.14 – 2.30) p = 0.007] during admission were more likely to exceed their predicted LOS. Multivariate analysis found that inhalation injury [3.73 (1.62 – 8.57) p = 0.002], diabetes [1.69 (1.04 – 2.77) p = 0.035], respiratory disease [1.86 (1.04 – 3.29) p = 0.035], psychiatric illness [1.50 (1.01 – 1.87) p = 0.04], alcohol use [2.18 (1.04 – 4.58) p = 0.04], and complications [3.63 (2.27 – 5.80) p < 0.0001] were contributing factors that increased the likelihood of patients exceeding predicted LOS. Diabetes was not a statistically significant contributor. Conclusions Progress has been made to identify patient and injury factors that increase the likelihood of increased LOS for older adult patients with burn injuries. This provides valuable data for physicians to better assess their patients and improve their quality of care. Applicability of Research to Practice Identification of key burn patient characteristics aids in prognostication. Determining length of stay aids with coordination of patient care with the multi-disciplinary team.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".