The Relationship between Higher Chronic Opioid Therapy Dose and Specific Personality Traits in Individuals with Chronic Pain
Bibliographic record
Abstract
Objective: To evaluate the relationship between opioid use and specific personality traits among individuals with chronic pain stratified by morphine equivalent doses (MEQ). Design: . Completion of the following outcome measures: Acceptance and Action Questionnaire, Anxiety Sensitivity Index, Brief-Coping with Problems Experience 28-item, Brief Pain Inventory Short Form, CAGE-AID substance misuse screening tool, EuroQol-5D, Generalized Anxiety Disorder 7-item, and Patient Health Questionnaire 9-item. One-way analysis of variance compared outcomes between MEQ groups. Results: 215 individuals (64.2% female) were included with a mean age of 52.7 ± 11.7 years and time since pain onset of 14.1 ± 10.2 years (range 1-45). There were no significant differences between MEQ groups with respect to sociodemographic and clinical health variables except for gender and employment status and time since pain onset. After controlling for gender, time since pain onset, and average pain severity, patients with MEQ 90+ mg had significantly higher scores for experiential avoidance and anxiety sensitivity in addition to increased pain interference, greater depressive and anxiety symptoms, more dysfunctional coping, and poorer QoL than those with MEQ 1-89 mg or MEQ 0 mg. Conclusions: Compared to individuals using no or lower-dose opioids to treat chronic pain, those using high-dose opioids had higher scores on two maladaptive personality traits (i.e., anxiety sensitivity and experiential avoidance) which was associated with poorer mood, greater pain interference, lower quality of life, and dysfunctional coping. These maladaptive personality traits may help to explain how individuals with chronic pain utilize higher doses of opioid analgesics.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".