Biomarkers of Pituitary Neoplasms: A Review (Part II)
Bibliographic record
Abstract
Several new markers have shown a capacity to predict the clinicopathological behavior of pituitary neoplasms; these markers have shown potential to correlate with tumor subtype and size and patient age and sex. These various markers are involved in a host of cellular functions, including cell-cycle progression, cell proliferation, apoptosis, cell adhesion, and tumor vascularity. In this companion article to our first review of Ki-67 as a marker of pituitary adenomas, we present and analyze the literature regarding matrix metalloproteinases and their inhibitors (tissue inhibitor metalloproteinases), vascular endothelial growth factor, fibroblast growth factor and its receptor, apoptotic markers and p53, as well as cyclooxygenase-2, galectin-3, and pituitary tumor transforming gene. Some of these markers, such as fibroblast growth factor and fibroblast growth factor receptor and matrix metalloproteinases, show particular promise in their ability to identify pituitary tumors that behave in an aggressive manner. We suggest the need for uniform design and application of methods and standardized criteria for the interpretation of results. A uniform approach will establish clinicopathological utility of emerging markers.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".