Clinical consensus guideline on the management of phaeochromocytoma and paraganglioma in patients harbouring germline SDHD pathogenic variants
Bibliographic record
Abstract
Patients with germline SDHD pathogenic variants (encoding succinate dehydrogenase subunit D; ie, paraganglioma 1 syndrome) are predominantly affected by head and neck paragangliomas, which, in almost 20% of patients, might coexist with paragangliomas arising from other locations (eg, adrenal medulla, para-aortic, cardiac or thoracic, and pelvic). Given the higher risk of tumour multifocality and bilaterality for phaeochromocytomas and paragangliomas (PPGLs) because of SDHD pathogenic variants than for their sporadic and other genotypic counterparts, the management of patients with SDHD PPGLs is clinically complex in terms of imaging, treatment, and management options. Furthermore, locally aggressive disease can be discovered at a young age or late in the disease course, which presents challenges in balancing surgical intervention with various medical and radiotherapeutic approaches. The axiom-first, do no harm-should always be considered and an initial period of observation (ie, watchful waiting) is often appropriate to characterise tumour behaviour in patients with these pathogenic variants. These patients should be referred to specialised high-volume medical centres. This consensus guideline aims to help physicians with the clinical decision-making process when caring for patients with SDHD PPGLs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".