Prevalence and correlates of clinical insomnia co‐occurring with chronic back pain
Bibliographic record
Abstract
Given the suggestion of a reciprocal relationship between sleep and pain and the recognition of sleep as an important parameter in determining quality of life, there is increasing research interest in sleep disturbance linked to chronic pain. The present study aimed to provide an estimate of the prevalence of 'clinical insomnia' in patients attending a specialist pain clinic and identify factors associated with it. Seventy chronic back pain patients and 70 gender- and age-matched pain-free controls completed a set of questionnaires measuring sleep (Insomnia Severity Index; ISI), pain (Short-Form McGill Pain Questionnaire) and a selection of general and specific psychological variables (Hospital Anxiety and Depression Scale, Short Health Anxiety Inventory). Scores suggestive of clinical insomnia (ISI > or = 15) were noted in 53% of chronic pain patients, when compared with only 3% in pain-free controls. Significant positive correlations with insomnia severity were detected for all six variables of interest (pain intensity, sensory pain ratings, affective pain ratings, general anxiety, general depression and health anxiety). Affective pain ratings and health anxiety were the best predictors of insomnia severity in this sample, accounting for 30% of the total variance, even when present pain intensity was controlled for. Affective pain remained as a significant predictor of insomnia severity when both the effect of pain intensity and the effects of anxiety and depression were controlled for. Future research should consider investigating the role of pain appraisal and health anxiety in the development and manifestation of insomnia concomitant to chronic pain.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".