Factors associated with opioid craving, opioid intake, and opioid misuse in patients with chronic pain: insights from an ecological momentary assessment study
Bibliographic record
Abstract
ABSTRACT: Many patients with chronic noncancer pain (CNCP) are prescribed opioid medication. However, concerns have been raised about the use of high opioid doses and the misuse of opioids in these patients. Research is needed to better understand the factors that influence day-to-day opioid intake patterns and opioid misuse behaviors in patients with CNCP. The first objective of this study was to examine the contribution of pain intensity, psychological factors, and physical dependence symptoms to daily opioid craving and opioid intake in patients with CNCP. The contribution of these factors to opioid misuse was also examined. In this ecological momentary assessment study, patients with CNCP prescribed short-acting opioids completed diaries, in between opioid doses, for 10 consecutive days. Diaries assessed a host of pain, psychological, and opioid-related variables. Diaries also assessed total daily morphine equivalent doses (MED) used by patients. Multilevel analyses indicated that intra-day increases in pain intensity, negative affect, catastrophizing, and withdrawal symptoms were associated with higher opioid craving (all P 's < 0.05). Day-to-day increases in pain intensity, catastrophizing, and craving were associated with greater opioid intake (ie, MED) (all P 's < 0.05). Patients' daily opioid craving contributed to daily opioid misuse even after accounting for other daily variables ( P < 0.05). Our findings provide new insights into the factors contributing to daily opioid craving, opioid intake, and opioid misuse among patients with CNCP. Interventions targeting these factors could potentially prevent opioid dose escalations and opioid-related harms among those maintained on opioid therapy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".