Identification of Predictors of Metastatic Potential in Paragangliomas to Develop a Prognostic Score (PSPGL)
Bibliographic record
Abstract
Context: Paragangliomas (PGLs) are rare tumors in adrenal and extra-adrenal locations. Metastasis are found in approximately 5% to 35% of PGLs, and there are no reliable predictors of metastatic disease. Objective: This work aimed to develop a prognostic score of metastatic potential in PGLs. Methods: A retrospective analysis was conducted of clinical data from a cohort with PGLs and tumor histological assessment. Patients were divided into metastatic PGL (presence of metastasis) and nonmetastatic PGL (absence of metastasis ≥96 months of follow-up) groups. Univariate and multivariable analysis were performed to identify predictors of metastatic potential. A prognostic score was developed based on coefficients of multivariable analysis. Kaplan-Meier curves were generated to estimate disease-specific survival (DSS). Results: Out of 263 patients, 35 patients had metastatic PGL and 110 patients had nonmetastatic PGL. In multivariable analysis, 4 features were independently related to metastatic disease and composed the Prognostic Score of Paragangliomas (PSPGL): presence of central or confluent necrosis (33 points), more than 3 mitosis/10 high-power field (HPF) (28 points), extension into adipose tissue (20 points), and extra-adrenal location (19 points). A PSPGL of 24 or greater showed similar sensitivity with higher specificity than the Pheochromocytoma of the Adrenal Gland Scaled Score (PASS) and Grading System for Adrenal Pheochromocytoma and Paraganglioma (GAPP). PSPGL less than or equal to 20 was associated with a risk of metastasis of approximately 10%, whereas a PSPGL of 40 or greater was associated with approximately 80%. The presence of metastasis and Ki-67 of 3% or greater were related to lower DSS. Conclusion: The PSPGL, composed of 4 easy-to-assess parameters, demonstrated good performance in predicting metastatic potential and good ability in estimating metastasis risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".