Large intragenic deletion of CDC73 (exons 4–10) in a three-generation hyperparathyroidism-jaw tumor (HPT-JT) syndrome family
Bibliographic record
Abstract
BACKGROUND: Inactivating mutations of CDC73 cause Hyperparathyroidism-Jaw Tumour syndrome (HPT-JT), Familial Isolated Hyperparathyroidism (FIHP) and sporadic parathyroid carcinoma. We conducted CDC73 mutation analysis in an HPT-JT family and confirm carrier status of the proband's daughter. METHODS: The proband had primary hyperparathyroidism (parathyroid carcinoma) and uterine leiomyomata. Her father and daughter had hyperparathyroidism (parathyroid adenoma) but no other manifestations of HPT-JT. CDC73 mutation analysis (sequencing of all 17 exons) and whole-genome copy number variation (CNV) analysis was done on leukocyte DNA of the three affecteds as well as the proband's unaffected sister. RESULTS: A novel deletion of exons 4 to 10 of CDC73 was detected by CNV analysis in the three affecteds. A novel insertion in the 5'UTR (c.-4_-11insG) that co-segregated with the deletion was identified. By in vitro assay the 5'UTR insertion was shown to significantly impair the expression of the parafibromin protein. Screening for the mutated CDC73 confirmed carrier status in the proband's daughter and the biochemistry and ultrasonography led to pre-emptive surgery and resolution of the hyperparathyroidism. CONCLUSIONS: A novel gross deletion mutation in CDC73 was identified in a three-generation HPT-JT family emphasizing the importance of including screening for large deletions in the molecular diagnostic protocol.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".