Day-to-day hedonic and calming effects of opioids, opioid craving, and opioid misuse among patients with chronic pain prescribed long-term opioid therapy
Bibliographic record
Abstract
ABSTRACT: Concerns have been raised regarding the misuse of opioids among patients with chronic pain. Although a number of factors may contribute to opioid misuse, research has yet to examine if the hedonic and calming effects that can potentially accompany the use of opioids contribute to opioid misuse. The first objective of this study was to examine the degree to which the hedonic and calming effects of opioids contribute to opioid misuse in patients with chronic pain. We also examined whether the hedonic and calming effects of opioids contribute to patients' daily levels of opioid craving, and whether these associations were moderated by patients' daily levels of pain intensity, catastrophizing, negative affect, or positive affect. In this longitudinal diary study, patients (n = 103) prescribed opioid therapy completed daily diaries for 14 consecutive days. Diaries assessed a host of pain, psychological, and opioid-related variables. The hedonic and calming effects of opioids were not significantly associated with any type of opioid misuse behavior. However, greater hedonic and calming effects were associated with heightened reports of opioid craving (both P's < 0.005). Analyses revealed that these associations were moderated by patients' daily levels of pain intensity, catastrophizing, and negative affect (all P's < 0.001). Results from this study provide valuable new insights into our understanding of factors that may contribute to opioid craving among patients with chronic pain who are prescribed long-term opioid therapy. The implications of our findings for the management of patients with chronic pain 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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".