Familial paraganglioma syndrome: applying genetic screening in otolaryngology.
Bibliographic record
Abstract
CONTEXT: sporadic head and neck paragangliomas often represent familial paraganglioma syndrome (FPS). FPS patients require close follow-up, and family members will benefit from screening. No clear guideline for following these patients exists in the otolaryngology literature. OBJECTIVE: to present a series of FPS patients, illustrating the importance of a cost-effective FPS genetic screening algorithm applicable in otolaryngology. DESIGN: case series, literature review, clinical guidelines. SETTING: tertiary care hospital. PATIENTS: adult patients with SDHx mutations were identified in the University of Alberta's head and neck mass database. Medical records were reviewed for presentation, diagnosis, findings, treatment, follow-up, and genetic testing. A literature review of FPS clinical features, treatment, and genetic screening methods was performed. INTERVENTION: genetic screening. MAIN OUTCOME MEASURE: cost-effectiveness of genetic testing for FPS screening. RESULTS: two patients with FPS were surgically treated by otolaryngologists. All patients presented with multifocal disease and carried SDHB or SDHD mutations. A screening and genetic testing protocol was implemented leading to early detection in a third patient, thus reducing morbidity. The literature review supports the contention that all patients with head and neck paragangliomas should undergo genetic testing. An algorithm to screen such patients is proposed. Cost analysis showed savings of over $2400 ($US 2200) every 6 years with this approach. CONCLUSION: owing to the potential morbidity associated with head and neck paragangliomas, it is prudent that FPS be ruled out. Those found to have SDHx mutations, and first-degree relatives, should be offered genetic testing with enrolment in a screening protocol. This provides a cost-effective, early detection approach to FPS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".