Prolactinoma and Adenomyosis – More than Meets the Eye: A Case Report
Bibliographic record
Abstract
Background/Objective: To report a case of adenomyosis in a woman with hyperprolactinemia which resolved after initiation of dopamine agonist therapy. Case Report: A 35-year-old woman with a history of Graves' disease was referred for evaluation of hyperthyroidism in March 2020. She was started on methimazole and thyroid function normalized. The patient also had a history of a pituitary microadenoma and was previously treated with cabergoline which was stopped after 12 months as she became pregnant.In July 2020, the patient began to have polymenorrhea. Hyperprolactinemia was thought to be an unlikely cause as it most often causes hypogonadotropic hypogonadism with amenorrhea. A pelvic ultrasound demonstrated a bulky uterus with adenomyosis. Gynecology recommended treating adenomyosis by lowering her prolactin levels. She was started on cabergoline 0.25 mg weekly in October 2021. Within 2 months of initiation of cabergoline, she had resolution of symptoms and radiological resolution of adenomyosis. Discussion: Prolactin has been implicated in the pathogenesis of adenomyosis, endometriosis and leiomyomas suggesting that a decrease in prolactin levels may suppress these lesions. The pathogenesis of adenomyosis has been related to direct prolactin effects in the promotion of gland/cell proliferation and function. Conclusion: We conclude that prolonged elevation in prolactin may result in the development of adenomyosis and subsequent prolonged abnormal uterine bleeding. Dopamine agonists, like cabergoline, inhibit the synthesis and secretion of prolactin from the pituitary gland and may have a role in the management of adenomyosis in patients with hyperprolactinemia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".