Aggressive Pituitary Tumors or Localized Pituitary Carcinomas: Defining Pituitary Tumors
Bibliographic record
Abstract
Pituitary tumors are common and exhibit a wide spectrum of hormonal, proliferative and invasive behaviors. Traditional classifications consider them malignant only when they exhibit metastasis. Patients who suffer morbidity and mortality from aggressive tumors classified as "adenomas" are denied support provided to patients with "cancers" and in many jurisdictions, these tumors are considered curiosities that do not warrant reporting in health registries. We propose use of the term "tumor" rather than "adenoma" to align with other neuroendocrine tumors. The features that can serve as diagnostic, prognostic and predictive markers are reviewed. Clinico-pathological and radiographic classifications provide important information and to date, no single biomarker has been able to offer valuable insight to guide the management of patients with pituitary tumors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".