Alcohol Use Disorder and Chronic Pain: An Overlooked Epidemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it