O-Methylguanine-DNA Methyltransferase Immunoexpression in a Double Pituitary Adenoma
Bibliographic record
Abstract
OBJECTIVE: Double pituitary adenomas in surgical cases are rarely reported. The incidence in published surgical specimens ranges from 0.4% to 1.3%. We present a treatment dilemma of a double adenoma that had differential O-methylguanine-DNA methyltransferase (MGMT) reactivity. CLINICAL PRESENTATION: A 48-year-old man presented with acromegaly and a recurrent pituitary adenoma. He had elevated growth hormone (GH) and elevated insulin-like growth factor blood levels and hyperprolactinemia. INTERVENTION: Subtotal transsphenoidal resection was performed. Morphologic examination disclosed 2 histologically distinct tumors, including a GH adenoma and a prolactin adenoma. Immunohistochemistry revealed Ki-67 labeling indices of 1% and 2%, respectively. Of significant note was MGMT immunopositivity in the GH adenoma and lack of staining in the prolactin adenoma. CONCLUSION: This is the first clinical instance in which MGMT was assessed in double adenomas of the pituitary. The 2 tumors showed significant differences in reactivity that could impact chemotherapeutic management. The adenomas underwent recurrence, a feature that reflects their invasive nature and the possibility that chemotherapeutic intervention may be required in the future. Response to temozolomide use is anticipated with respect to the prolactin adenoma but would likely not benefit the GH cell adenoma of our patient.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".