Is Gastric Bypass a Risk Factor for Complicated Alcohol Withdrawal? Case Report and Literature Review
Bibliographic record
Abstract
Alcohol use disorder and gastric bypass surgery are highly comorbid. Alcohol withdrawal syndrome (AWS) is a common and potentially life-threatening event, requiring nuanced and individually tailored management depending on various clinical factors including patient history, alcohol consumption, comorbidities, and timeline of use. Although increasingly common, the literature for managing alcohol withdrawal in the gastric bypass population is quite limited. We present the case of a 45-year-old woman with a past history of Roux-en-Y gastric bypass admitted for alcohol withdrawal at a psychiatric hospital who experienced a complicated withdrawal despite adhering to standard management guidelines. She had been consuming 8 to 12 standard drinks daily, and she was therefore monitored on a Clinical Institute Withdrawal Assessment for Alcohol. She experienced only minimal withdrawal symptoms up to 48 hours following cessation of alcohol consumption. At 70 hours postcessation, she experienced a witnessed tonic-clonic seizure with associated head trauma with internal bleeding, requiring acute medical intervention. This timeline of withdrawal symptoms is atypical, yet perhaps understood in the context of her past medical history which included gastric bypass surgery. We discuss the potential complicating factors inherent in individuals who have received Roux-en-Y gastric bypass in the past with respect to alcohol metabolism. We discuss the similar considerations with respect to altered metabolism of therapeutics commonly used in managing this condition. Lastly, we include a review of the extent literature on this topic and propose possible considerations for managing this unique but increasingly prevalent clinical scenario.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".