Mood and anxiety disorders associated with chronic pain: an examination in a nationally representative sample
Bibliographic record
Abstract
Chronic pain and psychiatric disorders frequently co-occur. However, estimates of the magnitude of these associations have been biased by the use of select clinical samples. The present study utilized the National Comorbidity Survey [Arch. Gen. Psychiatry 51 (1994) 8-19] Part II data set to investigate the associations between a chronic pain condition (i.e. arthritis) and common mood and anxiety disorders in a sample representative of the general US civilian population. Participants (N=5877) completed the Composite International Diagnostic Interview [World Health Organization (1990)], a structured interview for trained non-clinician interviewers based on the revised third edition of the Diagnostic and Statistical Manual of Mental Disorders [American Psychiatric Association (1987)], and provided self-reports of pain and disability associated with a variety of medical conditions. Significant positive associations were found between chronic pain and individual 12-month mood and anxiety disorders [odds ratios (OR) ranged from 1.92 to 4.27]. The strongest associations were observed with panic disorder (OR=4.27) and post-traumatic stress disorder (OR=3.69). The presence of one psychiatric disorder was not significantly associated with pain-related disability, but the presence of multiple psychiatric disorders was significantly associated with increased disability. The findings of the present study raise the possibility that improved efforts regarding the detection and treatment of anxiety disorders may be required in pain treatment settings.
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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 | 0.003 |
| 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 | 0.001 |
| 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".