Advances in Adrenal and Extra-adrenal Paraganglioma: Practical Synopsis for Pathologists
Bibliographic record
Abstract
Adrenal paraganglioma (or "pheochromocytoma") and extra-adrenal paraganglioma, collectively abbreviated PPGL, are rare but spectacular nonepithelial neuroendocrine neoplasms. These are the most inheritable neoplasia of all, with a metastatic potential in a varying degree. As of such, these lesions demand careful histologic, immunohistochemical, and genetic characterization to provide the clinical team with a detailed report taking into account the anticipated prognosis and risk of syndromic/inherited disease. While no histologic algorithm, immunohistochemical biomarker, or molecular aberration single-handedly can identify potentially lethal cases upfront, the combined analysis of various risk parameters may stratify PPGL patients more stringently than previously. Moreover, the novel 2022 WHO Classification of Endocrine and Neuroendocrine Tumors also brings some new concepts into play, not least the reclassification of special neuroendocrine neoplasms (cauda equina neuroendocrine tumor and composite gangliocytoma/neuroma-neuroendocrine tumor) previously thought to belong to the spectrum of PPGL. This review focuses on updated key diagnostic and prognostic concepts that will aid when facing this rather enigmatic tumor entity in clinical practice.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".