Quality of sleep and its daily relationship to pain intensity in hospitalized adult burn patients
Bibliographic record
Abstract
Sleep disturbances are frequently reported in victims following burn injuries. This prospective study was designed to assess sleep quality and to examine its daily relationship to pain intensity within the first week of hospitalization. Twenty-eight non-ventilated patients were interviewed during 5 consecutive mornings (number of observations=140) to collect information about perceived quality of sleep (visual analogue scale, number of hours, number of awakenings, presence of nightmares). Pain intensity was assessed at rest (nighttime, morning, during the day) and following therapeutic procedures using a 0-10 numeric scale. Seventy-five percent of patients reported sleep disturbances at some point during the study although, in most patients, sleep quality was not consistently poor. Pooled cross-section regression analyses showed significant temporal relationships between quality of sleep and pain intensity such that a night of poor sleep was followed by a significantly more painful day. Pain during the day was not found to be a significant predictor of poor sleep on the following night. These results support previous findings that perceived quality of sleep following burn injury is poor. Moreover, they show a daily relationship between quality of sleep and acute burn pain in which poor sleep is linked to higher pain intensity during the day.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".