Cost analysis of DNA‐based testing in a large Canadian family with multiple endocrine neoplasia type 2
Bibliographic record
Abstract
One of the major goals of genetic testing is the reduction of morbidity and mortality. Given the appropriate circumstances, this can result in reduction in health care costs. Such savings can be demonstrated most effectively in large families with mutations in well characterized, dominantly acting genes. In our large family, a point mutation TGC>CGC in exon 10 of the RET proto-oncogene, which results in a missense mutation (Cys620Arg), was identified in two individuals. The proband has medullary thyroid carcinoma (MTC), as did her deceased mother. One son has MTC and Hirschsprung's disease. The proband's mother had nine siblings; the proband has three siblings, another son, and 69 maternal cousins. Genetic testing has been performed on the closest relatives and has identified four individuals with, and 54 individuals without, a familial RET mutation. Significant cost savings have been realized in both genetic testing and clinical surveillance. In this family, for every at-risk individual identified as a true-negative, the minimum yearly savings in clinical surveillance is 508 dollars per person. As demonstrated by this case, economic costs of genetic diagnostics should take into account the potential saved monies in tests, both molecular and clinical.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".