Genetic testing for Lynch syndrome in the province of Ontario
Bibliographic record
Abstract
BACKGROUND: In November 2001, genetic testing for Lynch syndrome (LS) was introduced by the Ministry of Health and Long-Term Care (MOH) in Ontario for individuals at high risk for LS cancers according to either tumor immunohistochemistry staining or their family history. This article describes the outcomes of the program and makes recommendations for improving it and informing other public health care programs. METHODS: Subjects were referred for molecular testing of the mismatch repair (MMR) genes MutL homolog 1, MutS homolog 2, and MutS homolog 6 if they met 1 of 7 MOH criteria. Testing was conducted from January 2001 to March 2015 at the Molecular Diagnostic Laboratory of Mount Sinai Hospital in Toronto. RESULTS: A total of 1452 subjects were tested. Of the 662 subjects referred for testing because their tumor was immunodeficient for 1 or more of the MMR genes, 251 (37.9%) carried a germline mutation. In addition, 597 subjects were tested for a known family mutation, and 298 (49.9%) were positive; 189 of these 298 subjects (63.4%) were affected with cancer at the time of testing. An additional 193 subjects were referred because of a family history of LS, and 34 of these (17.6%) had a mutation identified. CONCLUSIONS: These results indicate that the provincial criteria are useful in identifying LS carriers after an MMR-deficient tumor is identified. Placing greater emphasis on testing unaffected relatives in families with a known mutation may identify more unaffected carriers and facilitate primary prevention in those individuals. Cancer 2016;122:1672-9. © 2016 American Cancer Society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".