Pain Experiences and Their Relation to Opioid Misuse Risk and Emotion Dysregulation
Bibliographic record
Abstract
Pain is a complex, multidimensional experience but often is measured as a unidimensional experience. This study aimed to separately assess the sensory and affective components of pain and identify their relations to important pain-related outcomes, particularly in terms of opioid misuse risk and emotion dysregulation among patients with chronic pain receiving treatment in Appalachia. Two hundred and twelve patients presenting to a multidisciplinary pain center completed the Difficulties in Emotion Regulation Scale (DERS-18), Screener and Opioid Assessment for Patients with Pain—Revised (SOAPP-R), and short-form McGill Pain Questionnaire (SF-MPQ). The sensory experience of pain was unrelated to emotion dysregulation (r = 0.06, <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>p</a:mi> <a:mo>=</a:mo> <a:mn>0.57</a:mn> </a:math> ) and weakly related to opioid misuse risk (r = 0.182, <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>p</c:mi> <c:mo><</c:mo> <c:mn>0.05</c:mn> </c:math> ). In contrast, the affective experience of pain was moderately related to emotion dysregulation (r = 0.217, <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>p</e:mi> <e:mo><</e:mo> <e:mn>0.05</e:mn> </e:math> ) and strongly related to opioid misuse risk (r = 0.37, <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mi>p</g:mi> <g:mo><</g:mo> <g:mn>0.01</g:mn> </g:math> ). In addition, emotion dysregulation predicted variance in opioid misuse risk above and beyond the affective and sensory experiences of pain ((b = 0.693, <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M5"> <i:mi>p</i:mi> <i:mo><</i:mo> <i:mn>0.001</i:mn> </i:math> ). The results suggest patients with a strong affective experience versus sensory experience of pain and challenges with emotion regulation may require a more comprehensive intervention to address these underlying components in order to reduce their risk of misusing opioid medications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".