Serotonin Syndrome Masquerading as Alcohol Withdrawal: A Case Report
Bibliographic record
Abstract
INTRODUCTION: Alcohol withdrawal syndrome (AWS) and serotonin syndrome (SS) share several overlapping symptoms, complicating diagnosis in patients with alcohol use disorder (AUD) on serotonergic treatment. CASE PRESENTATION: We describe a 54-year-old male with a history of AUD and anxiety disorder who presented to a residential treatment center after patient report about 11 days of alcohol abstinence. Despite an initially mild withdrawal course, he developed worsening tremors, nausea, diarrhea, diaphoresis, muscle twitching, rigidity, and restlessness beyond the typical AWS timeframe. His medication regimen included multiple serotonergic agents. Neurological examination revealed hyperreflexia, clonus, and persistent hypertension, fulfilling the Hunter Serotonin Toxicity Criteria for SS. All serotonergic medications were discontinued and supportive care was initiated, leading to rapid symptom improvement and resolution. CONCLUSIONS: Thorough evaluation of medication history and symptom timeline during clinical assessment is critical for differentiating AWS and SS. Clinicians are encouraged to remain vigilant for SS in patients with AUD on serotonergic agents to prevent adverse outcomes and potential mortality.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| 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".