Self-reports of medication side effects and pain-related activity interference in patients with chronic pain
Bibliographic record
Abstract
The primary purpose of this study was to examine the association between self-reports of medication side effects and pain-related activity interference in patients with chronic pain. The potential moderators of the association between reports of side effects and pain-related activity interference were also examined. A total of 111 patients with chronic musculoskeletal pain were asked to provide, once a month for a period of 6 months, self-reports of medication use and the presence of any perceived side effects (eg, nausea, dizziness, headaches) associated with their medications. At each of these time points, patients were also asked to provide self-reports of pain intensity, negative affect, and pain-related activity interference. Multilevel modeling analyses revealed that month-to-month increases in perceived medication side effects were associated with heightened pain-related activity interference (P < 0.05). Importantly, multilevel models revealed that perceived medication side effects were associated with heightened pain-related activity interference even after controlling for the influence of patient demographics, pain intensity, and negative affect. This study provides preliminary evidence that reports of medication side effects are associated with heightened pain-related activity interference in patients with chronic pain beyond the influence of other pain-relevant variables. The implications of our findings for clinical practice and the management of patients with chronic pain conditions are discussed.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".