Hot trends in pheochromocytoma and paraganglioma: are we getting closer to personalized dynamic prognostication?
Bibliographic record
Abstract
Pheochromocytoma and abdominal paraganglioma (PPGL) are rare catecholamine-producing, keratin-negative, non-epithelial neuroendocrine neoplasms characterized by a unique association with syndromic diseases caused by constitutional mutations in a wide range of susceptibility genes. While PPGLs are recognized for their malignant potential, the risk of metastatic disease varies depending on several clinical, histological, and genetic factors. Accurate diagnosis and prognosis of these tumors require a multidisciplinary approach, integrating insights from various medical specialties. Pathologists play a crucial role in this complex task, as numerous morphological, immunohistochemical, and genetic findings can be linked to worse outcomes. Therefore, it is vital to stay informed about the latest advancements in PPGL pathology. This brief review provides an overview of the challenges associated with PPGLs and highlights the most recent developments in tumor prognostication.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".