Genetic Analysis in the Diagnosis of Familial Paragangliomas
Bibliographic record
Abstract
OBJECTIVES: In the management of two related patients with multicentric glomus jugulare tumors, given the incidence of 1:30,000 with approximately 20% familial cases, our objective was to review the genetic characteristics and inheritance patterns of these tumors and to determine what molecular genetic screening possibilities exist for the phenotypically normal family members. In addition, our aim was to review the incidence of various multicentric paraganglioma (PGL) tumor location combinations. METHODS: Molecular genetic linkage analysis testing was performed on the 2 patients and 14 other unaffected family members. We report the results of this screening and review the literature on the incidence and genetics of paragangliomas. RESULTS: The inheritance pattern in the literature demonstrates autosomal dominant transmission with maternal imprinting (inactivation). The proclivity for multicentric origin increases to 26% in familial cases, as reflected in our patients. In addition to the two patients, four unaffected family members demonstrated the presence of the disease haplotype at chromosome band 11q23, which indicates a very high likelihood of developing a paraganglioma, given the highly penetrant nature of the disease. CONCLUSIONS: It is clear that the familial PGL gene locus is situated at chromosome 11q23. The gene itself and its exact degree of penetrance, however, still await identification. Since early detection of paragangliomas reduces the incidence of morbidity and mortality, genotypic analysis as a screening tool in families of affected patients should play a front-line diagnostic role, leading to more timely and cost-effective patient management.
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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) | 0.000 | 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 | 0.001 | 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".