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.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".