Sleep quality in subjects suffering from chronic pain
Bibliographic record
Abstract
BACKGROUND: Sleeping problems are very common in patients with chronic pain. The aim of the study was to investigate the association between different dimensions of chronic pain and sleep quality in chronic pain patients. METHODS: In this cross-sectional interview-based questionnaire study, patients from 3 different pain treatment centers in Vienna aged 18-65 years, with pain lasting 3 months or longer were asked to participate. The association between the short-form McGill pain questionnaire (SF-MPQ) and sleep quality (sleep onset latency, interrupted sleep due to pain, sleep duration and recovering effect of sleep) was assessed. RESULTS: In this study 121 patients (male 32, female 89, mean age 49 ± 9 years) could be analyzed. Of the patients 38.8% needed more than 30 min for falling asleep, 63.6% reported sleep fragmentation, 30.6% slept less than 5 h and 60.3% reported no recovering effect of sleep. The strongest associations between pain characteristics and sleep quality were found for pain intensity and affective pain aspects. Logistic regression analyses revealed that one point more in the total score of SF-MPQ increased the odds of needing more than 30 min for falling asleep, waking up more than 3 times due to pain, sleeping less than 5 h, and perceiving the sleep as non-recovering, by 6%. Adjusting for physical and psychological quality of life lowered the odds ratios and the association was no longer significant. CONCLUSION: The results underline the importance of paying attention to sleep quality in patients with chronic pain. The results also indicate that psychological factors might mediate the association between pain and sleep quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".