Alcohol Use Disorder and Chronic Pain: An Overlooked Epidemic
Bibliographic record
Abstract
Alcohol use disorder (AUD) and chronic pain disorders are pervasive, multifaceted medical conditions that often co-occur. However, their comorbidity is often overlooked, despite its prevalence and clinical relevance. Individuals with AUD are more likely to experience chronic pain than the general population. Conversely, individuals with chronic pain commonly alleviate their pain with alcohol, which may escalate into AUD. This narrative review discusses the intricate relationship between AUD and chronic pain. Based on the literature available, the authors present a theoretical model explaining the reciprocal relationship between AUD and chronic pain across alcohol intoxication and withdrawal. They propose that the use of alcohol for analgesia rapidly gives way to acute tolerance, triggering the need for higher levels of alcohol consumption. Attempts at abstinence lead to alcohol withdrawal syndrome and hyperalgesia, increasing the risk of relapse. Chronic neurobiological changes lead to preoccupation with pain and cravings for alcohol, further entrenching both conditions. To stimulate research in this area, the authors review methodologies to improve the assessment of pain in AUD studies, including self-report and psychophysical methods. Further, they discuss pharmacotherapies and psychotherapies that may target both conditions, potentially improving both AUD and chronic pain outcomes simultaneously. Finally, the authors emphasize the need to manage both conditions concurrently, and encourage both the scientific community and clinicians to ensure that these intertwined conditions are not overlooked given their clinical significance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".