Major Depression and Insomnia in Chronic Pain
Bibliographic record
Abstract
OBJECTIVES: Insomnia and depression are common problems for people with chronic pain, and previous research has found that each is correlated with measures of pain and disability. The goal of this study was to examine the combined impact of major depression and insomnia on individuals with chronic pain. METHODS: The participants were patients with chronic musculoskeletal pain who underwent evaluation at an interdisciplinary treatment center. On the basis of semistructured interviews, participants were classified in three groups depending on whether they: (1) met criteria for major depression with insomnia (n = 38); (2) had insomnia without major depression (n = 58); or (3) had neither insomnia nor major depression (n = 47). The groups were then compared on self-report measures that included the McGill Pain Questionnaire, the Beck Depression Inventory, and the Multidimensional Pain Inventory. RESULTS: Participants with major depression and insomnia reported the most difficulty on measures of affective distress, life control, interference, and pain severity, although the insomniac patients without major depression also had elevated scores on some measures. In regression analyses, insomnia severity ratings did not contribute uniquely to the prediction of psychosocial problems when depression was controlled, but they did contribute to the prediction of pain severity. CONCLUSIONS: These results suggest that patients with chronic pain and concurrent major depression and insomnia report the highest levels of pain-related impairment, but insomnia in the absence of major depression is also associated with increased pain and distress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".