Regular use of prescribed opioids: Association with common psychiatric disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Use of opioids for chronic non-cancer pain is increasing, but the clinical epidemiology and standards of care for this practice are poorly defined. Psychiatric disorders are associated with increased physical symptoms and may be associated with opioid use. We performed a secondary analysis of cross-sectional data from the Health Care for Communities (HCC) survey conducted in 1997-1998 (N=9279) to determine the association of psychiatric disorders and self-reported regular use of prescribed opioids within the past year. Regular prescription opioid use was reported by 282 (3%) respondents. In unadjusted logistic regression models, respondents with common mental disorders in the past year (major depression, dysthymia, generalized anxiety disorder, or panic disorder) were more likely to report regular prescription opioid use than those without any of these disorders (OR=6.15, 95% CI=4.13, 9.14, P< 0.001). Respondents reporting problem drug use (OR=4.75, 95% CI=2.52, 8.94, P<0.001), or problem alcohol use (OR=1.89, 95% CI=1.03, 3.40, P=.041) reported higher rates of prescribed opioid use than those without problem use. In multivariate logistic regression models controlling for demographic and clinical variables, the presence of a common mental disorder remained a significant predictor of prescription opioid use (OR=3.15, 95% CI=1.69, 5.88, P<0.001), among individuals reporting low pain interference (N=8307); but not (OR=1.27, n.s.) among those reporting high pain interference (N=972). Depressive, anxiety and drug abuse disorders are associated with increased use of regular opioids in the general population. Depressive and anxiety disorders are more common and more strongly associated with prescribed opioid use than drug abuse disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it