Pituitary tumors in patients with MEN1 syndrome
Bibliographic record
Abstract
We briefly review the characteristics of pituitary tumors associated with multiple endocrine neoplasia type 1. Multiple endocrine neoplasia type 1 is an autosomal-dominant disorder most commonly characterized by tumors of the pituitary, parathyroid, endocrine-gastrointestinal tract, and pancreas. A MEDLINE search for all available publications regarding multiple endocrine neoplasia type 1 and pituitary adenomas was undertaken. The prevalence of pituitary tumors in multiple endocrine neoplasia type 1 may vary from 10% to 60% depending on the studied series, and such tumors may occur as the first clinical manifestation of multiple endocrine neoplasia type 1 in 25% of sporadic and 10% of familial cases. Patients were younger and the time between initial and subsequent multiple endocrine neoplasia type 1 endocrine lesions was significantly longer when pituitary disease was the initial manifestation of multiple endocrine neoplasia type 1. Tumors were larger and more invasive and clinical manifestations related to the size of the pituitary adenoma were significantly more frequent in patients with multiple endocrine neoplasia type 1 than in subjects with non-multiple endocrine neoplasia type 1. Normalization of pituitary hypersecretion was much less frequent in patients with multiple endocrine neoplasia type 1 than in subjects with non-multiple endocrine neoplasia type 1. Pituitary tumors in patients with multiple endocrine neoplasia type 1 syndrome tend to be larger, invasive and more symptomatic, and they tend to occur in younger patients when they are the initial presentation of multiple endocrine neoplasia type 1.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".