Management of a microprolactinoma with aripiprazole in a woman with cabergoline-induced mania
Bibliographic record
Abstract
UNLABELLED: A previously healthy 32-year-old woman developed cyclical mood swings after being prescribed cabergoline for a pituitary microprolactinoma. These mood swings persisted for over 2 years, at which point she developed an acute manic episode with psychotic features and was admitted to a psychiatry unit. Cabergoline was discontinued and replaced with aripiprazole 10 mg/day. Her manic episode quickly resolved, and she was discharged within 6 days of admission. The aripiprazole suppressed her prolactin levels for over 18 months of follow-up, even after the dose was lowered to 2 mg/day. There was no significant change in tumor size over 15 months, treatment was well tolerated and the woman remained psychiatrically stable. LEARNING POINTS: Dopamine agonists such as cabergoline, which are a standard treatment for microprolactinomas, can have serious adverse effects such as psychosis or valvular heart disease.Aripiprazole is a well-tolerated atypical antipsychotic that, unlike other antipsychotics, is a partial dopamine agonist capable of suppressing prolactin levels.Adjunctive, low-dose aripiprazole has been utilized to reverse risperidone-induced hyperprolactinemia.This case report demonstrates how aripiprazole monotherapy, in doses ranging from 2 to 10 mg/day, was effective in suppressing prolactin in a woman with a microprolactinoma who developed psychiatric side effects from cabergoline.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".