Gonadotroph Tumor Associated with Multiple Endocrine Neoplasia Type 1
Bibliographic record
Abstract
Although anterior pituitary tumors constitute a main clinical feature of multiple endocrine neoplasia type 1 (MEN1), and most types of pituitary tumors have been associated with MEN1, gonadotroph tumors have not previously been recognized clinically as part of this syndrome. We report here a woman who presented with ovarian hyperstimulation due to a gonadotroph tumor that was confirmed biochemically and immunohistochemically. She then developed hyperparathyroidism, and she was found to have three hypercellular parathyroid glands. Subsequently, she developed a temporal lobe metastasis of the gonadotroph tumor, demonstrating that it was a gonadotroph carcinoma. The diagnosis of MEN1 was confirmed by finding a deletion mutation (c.307delC) on the second exon of the MEN1 gene that predicts truncation of the resulting menin protein 15 codons downstream from the deletion (p.Leu103fsX15). This case illustrates that gonadotroph tumors, like other pituitary tumors, can be part of MEN1. The clinical implications of this case are that the clinical and biochemical features of gonadotroph tumors should be considered when evaluating patients for MEN1, and MEN1 should be considered in patients who have gonadotroph tumors.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".