Temozolomide in aggressive pituitary adenomas and carcinomas
Bibliographic record
Abstract
Temozolomide is an alkylating agent used in the treatment of gliomas and, more recently, aggressive pituitary adenomas and carcinomas. Temozolomide methylates DNA and, thereby, has antitumor effects. O6-methylguanine-DNA methyltransferase, a DNA repair enzyme, removes the alkylating adducts that are induced by temozolomide, thereby counteracting its effects. A Medline search for all of the available publications regarding the use of temozolomide for the treatment of pituitary tumors was performed. To date, 46 cases of adenohypophysial tumors that were treated with temozolomide, including 30 adenomas and 16 carcinomas, have been reported. Eighteen of the 30 (60%) adenomas and 11 of the 16 (69%) carcinomas responded favorably to treatment. One patient with multiple endocrine neoplasia type 1 and an aggressive prolactin-producing adenoma was also treated and demonstrated a good response. No significant complications have been attributed to temozolomide therapy. Thus, temozolomide is an effective treatment for the majority of aggressive adenomas and carcinomas. Evidence indicates that there is an inverse correlation between levels of O6-methylguanine-DNA methyltransferase immunoexpression and therapeutic response. Alternatively, high-level O6-methylguanine-DNA methyltransferase immunoexpression correlates with an unfavorable response. Here, we review the use of temozolomide for treating pituitary neoplasms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".