Bibliographic record
Abstract
The sellar region is the site of a large number of pathological entities arising from the pituitary and adjacent anatomical structures, including brain, blood vessels, nerves and meninges. The surgical pathology of this area requires the accurate identification of neoplastic lesions, including pituitary adenoma and carcinoma, craniopharyngioma, neurological neoplasms, germ cell tumours, haematological malignancies and metastases, as well as non-neoplastic lesions such as cysts, hyperplasias and inflammatory disorders. This review provides a practical approach to the diagnosis of pituitary specimens that are sent to the pathologist at the time of surgery. The initial examination requires routine haematoxylin and eosin staining to establish whether the lesion is a primary adenohypophysial proliferation or one of the many other pathologies that occurs in this area. The most common lesions resected surgically are pituitary adenomas. These are evaluated with several special stains and immunohistochemical markers that are now available to accurately classify these pathologies. The complex subclassification of pituitary adenomas is now recognised to reflect specific clinical features and genetic changes that predict targeted treatments for patients with pituitary disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".