Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it