Low immunohistochemical expression of MGMT in ACTH secreting pituitary tumors of patients with Nelson syndrome
Bibliographic record
Abstract
Objective MGMT expression in brain and pituitary tumors has been correlated with temozolomide treatment. Few medical therapies are available in patients with Nelson Syndrome. The aim of the present study was to assess immunohistochemical expression of MGMT in ACTH‐secreting pituitary adenomas in patients with Nelson Syndrome. Methods Our material consisted of specimens from ACTH‐secreting pituitary adenomas from patients with Nelson Syndrome. Immunohistochemical staining for MGMT was performed using the streptavidin‐biotin‐peroxidase complex method. MGMT immunoreactivity was assessed microscopically and recorded as an estimated percentage of nuclear MGMT immunopositivity. (0=none, 1=<10%, 3=<50%, 4=>50%) Results Male:Female ratio was 3:5, with average patient age being 62 (range 57–66). Five of the eight specimens (65%) exhibited no MGMT immunoreactivity, with two out of eight cases (25%) showing slight MGMT immunopositivity and one out of eight cases (12%) demonstrating moderate MGMT immunopositivity (<25%). Conclusions Temozolomide therapy may be useful in patients with Nelson Syndrome. Absent or low MGMT staining in brain and other neoplasms has been shown to correlate with successful treatment with temozolomide, and recent reports assessing aggressive pituitary adenomas suggest similar outcome.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".