Tumors of the Pituitary Gland
Bibliographic record
Abstract
Anterior pituitary tumors are clonal proliferation of pituitary cells. They usually consist of one cell type, although some adenomas consist of more than one cell type. Pituitary tumors can be characterized by broad spectrum markers such as synaptophysin and chromogranin. Reticulin histochemical staining is useful in separating normal hyperplastic and neoplastic pituitary tissues. Electron microscopy is a powerful tool to help separate various subtypes of adenomas including sparsely and densely granulated growth hormone adenomas and different subtypes of silent ACTH adenomas. The major types of pituitary adenomas include GH, PRL, ACTH, TSH, gonadotroph (FSH/LH), and null cell adenomas. A combination of hematoxylin and eosin staining, immunohistochemistry, and electron microscopic studies are the most comprehensive ways of classifying pituitary tumors.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".