Molecular Biology of Pituitary Tumors
Bibliographic record
Abstract
Pituitary neoplasms are relatively common tumors that demonstrate a wide range of hormonal and proliferative behaviors. The diversity in their hormonal activity reflects their complex cytodifferentiation. It was believed that 1 cell of adenohypophysis could produce only 1 hormone. However, advances in immunocytochemistry, electron microscopy, and molecular studies have led to a new classification of pituitary tumors and a better understanding of the mechanisms that determine development of these neoplasms. The heterogeneity of pituitary adenoma subtypes suggested that no single common etiologic event was responsible for the development of adenoma. Molecular studies within the last decade have provided evidence that multiple molecular events are involved in the pathogenesis of pituitary adenomas. These series of molecular events supported the modern theory of multistep tumorigenesis. The objective of this review is to shed light on the molecular pathogenesis and histologic classification of adenohypophyseal neoplasms based on the recent biochemical results, molecular studies, and clinicopathologic findings. Understanding the genetic defects of pituitary tumors allows a deeper insight in the pathogenesis and biologic behavior of pituitary tumors and can lead to the development of a novel approach for the management of patients with pituitary tumors.
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.
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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".