Varenicline induced auditory hallucinations in a young female with bipolar disorder: a case report
Bibliographic record
Abstract
BACKGROUND: Creating appropriate and sustainable treatment plans for patients with concurrent disorders presents a challenge to psychiatrists and addiction medicine specialists alike. Although varenicline has been found to be the most effective medication for smoking cessation and abstinence when compared to results from placebo medications, nicotine patches and bupropion, caution is needed when starting patients on this medication. With the high prevalence of concurrent mental health and substance use disorders in vulnerably-housed populations in Canada, it becomes increasingly important to advocate for increased guidance and research into treating concurrent disorders. CASE PRESENTATION: In this case, a young female patient provisionally diagnosed with bipolar I disorder was hospitalized for a manic episode in the context of substance use and medication noncompliance. She also endorsed a long history of tobacco, alcohol, cocaine, cannabis and ketamine use. Perceptual abnormalities, including auditory hallucinations, were not recorded at admission. In addition to being stabilized for bipolar diagnosis, the patient was started on nicotine replacement therapy on Day 7 of admission followed by initiation of varenicline for smoking cessation on Day 14 of admission. Soon after the varenicline treatment was started, the patient developed auditory hallucinations, paranoia and referential beliefs. However, her insight was intact, and she had minimal thought form disorganization. In this case, these symptoms were thought to be secondary to varenicline after the consideration of potential alternative contributors. CONCLUSION: The occurrence of side effects as a result of varenicline use in patients with diagnosed mental health conditions is rare and underlying psychiatric illness is not labeled as an absolute contraindication in the prescription of varenicline. However, it is important to advocate for increased guidance and research on the treatment of substance use disorders in patients with bipolar I disorder. Patients may also benefit from increased collaboration between psychiatric and addiction services as that may allow for earlier recognition and intervention of symptoms to minimize distress.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".