Sleep Quality Among Burn Survivors and the Importance of Intervention: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Burn survivors undergo a plethora of physiologic disturbances that can greatly affect the quality of life (QOL) and healing processes. This review aimed to systematically examine sleep quality among individuals with burns and to explore the effectiveness of interventions using a meta-analytic approach. A systematic review of the literature was conducted by searching for articles using various databases. Titles and abstracts were screened and full texts of retained articles were assessed based on eligibility criteria. Methodological quality was ascertained in all articles using various scales. Overall, 5323 articles were screened according to titles and abstracts and 25 articles were retained following full-text screening. Of the 25 articles, 17 were assessed qualitatively, while 8 were included in the meta-analysis. Based on the qualitative analysis, sleep was found to be negatively affected in patients with burn injuries. The subsample of eight articles included in the meta-analysis showed an overall weighted mean effect size (Hedges's g) of 1.04 (SE = 0.4, 95% CI, z = 3.0; P < 0.01), indicating a large, positive effect of the intervention on sleep quality for patients with burn injuries. This review was able to demonstrate the detrimental effects of burn injury on sleep quality. Several interventions have been examined throughout the literature and have shown to be beneficial for sleep quality. However, there is great heterogeneity between existing interventions. The results from this review suggest that further research is needed before recommendations can be made as to which intervention is most effective at improving sleep in patients suffering from burn injuries.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".