Frailty as a predictor of adverse outcomes in burn patients: a systematic review
Bibliographic record
Abstract
BACKGROUND: The identification of new prognostic tools for the prediction of burn patients' morbidity outcomes is necessary. Considering the feasibility of frailty assessment in the clinical setting, we aim to systematically review the literature on the associations between frailty and adverse outcomes in burn patients. METHODS: Studies were retrieved from MEDLINE (through PubMed), Web of Science, Scopus, and Embase from their inception up to 8 September 2022. Included studies were those that used frailty indices to predict adverse outcomes in burn patients. The quality assessment was done using the National, Heart, Lung, and Blood Institute (NHLBI) checklist. The results were synthesized narratively. RESULTS: We included 18 studies. The sample size among the included studies varied between 42-1615 patients. There were 12 research articles and 6 conference abstracts. Most of the studies were recently published in 2021 and 2022. Seven different frailty measures were evaluated. The following frailty measures were used: Canadian Study for Health and Ageing (CSHA) Clinical Frailty score (CFS), Modified frailty index-11 (mFI-11), Hospital frailty index, FRAIL scale, Emergency General Surgery Frailty Index (EGSFI), and Burn frailty index (BFI). There was only one report regarding a specific frailty index designed for the burn population (BFI). Except for one study (which used mFI-11), all included studies have shown a significant effect between assessing frailty and predicting worse outcomes. The CFS was an independent predictor of mortality among the burn population with high certainty of evidence. We found a significant association for other frailty indices as a predictor of mortality, however, the certainty of evidence regarding those was not high. Eight studies found a positive association between assessing frailty and unfavorable discharge location. There was no association between frailty and increased length of stay. CONCLUSION: In conclusion, the postadmission assessment of frailty can be a reliable tool for predicting unfavorable outcomes and mortalities among patients with burn injuries. In addition, future studies with various populations from other countries are required to evaluate the efficacy of frailty indices measurement in order to strengthen the available evidence.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".