Influence of opioid-related side effects on disability, mood, and opioid misuse risk among patients with chronic pain in primary care
Bibliographic record
Abstract
Abstract Background: There is increasing concern among primary care practitioners about the use of opioids for chronic pain, including their adverse effects, but little attention has been given to how reports of side effects from prescription medication can contribute to outcomes among patients with chronic pain. The aim of this study was to investigate the impact of frequently reported side effects on mood, disability, and opioid misuse in patients with chronic pain prescribed opioids within primary care. Methods: Two hundred (N = 200) patients with chronic pain taking opioids for pain were recruited into the study. All patients completed baseline measures and a monthly side effects checklist once a month for 6 months. Patients were divided evenly based on a median split of the number of endorsed side effects over 6 months. The subjects repeated the baseline measures at the end of the study period. Results: Over time, reports of medication side effects tended to decrease, but differences in frequency of reported side effects from baseline to follow-up (6-month time) were not significant, and the order of the frequency of the reported side effects remained similar. Patients who reported significant medication-related adverse effects reported significantly greater activity interference, negative affect, and catastrophizing compared with those with fewer side effects (P < 0.01). In addition, those patients with pain who reported more side effects showed significantly higher scores on opioid misuse risk (P < 0.001). Discussion: This study demonstrates the important role of monitoring medication-related side effects among patients with chronic pain who are prescribed opioid medication for pain within primary care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".