Changes in sleep architecture after burn injury: ‘Waking up’ to this unaddressed aspect of postburn rehabilitation in the developing world
Bibliographic record
Abstract
Background Changes in sleep architecture are common phenomena observed in post-traumatic patients; such altered sleeping patterns have negative implications on various phases of rehabilitation. Sleep is an essential process, without which one cannot function effectively and, hence, any aberrations in the quality of sleep in such patients need to be critically analyzed. Objective To probe the quality of sleep in postburn patients at one year compared with a group of adequately matched controls. Methods Quality of sleep in postburn patients at one year was measured using the Pittsburg Sleep Quality Index questionnaire and compared with a group of adequately matched controls. Data were tabulated and subjected to statistical analysis using Pearson's χ 2 test. Results The relationship between the postburn state and sleep disturbances was found to be statistically significant. Other relevant parameters are also highlighted and discussed. Discussion Sleep is one of aspect of functioning that may be least taken into account by professionals during the phase of postburn rehabilitation because more obvious threats receive preferred treatment. Unless these problems are dealt with in the postburn period, rehabilitation can never be complete. Conclusion Postburn patients experience significant changes in sleep architecture, which need to be taken into account to enable complete rehabilitation of the patient.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".