Canadian consensus for the assessment and testing of Lynch syndrome
Bibliographic record
Abstract
BACKGROUND: Lynch syndrome (LS) is an autosomal dominant cancer predisposition syndrome caused by a germline pathogenic variant, or epigenetic silencing, of a mismatch repair (MMR) gene, leading to a wide cancer spectrum with gene-specific penetrance. Ascertainment, assessment and testing of LS individuals is complex. A Canadian national guideline is needed to ensure equitable access to patient care across the country. METHODS: The Canadian Lynch Syndrome (CDN-LS) working group was formed in 2021, consisting of 37 multidisciplinary LS experts and patient partners. To formulate consensus statements, a national environmental scan, Canadian clinical survey and literature review were undertaken. The e-Delphi method was used to reach consensus statements among the CDN-LS group. RESULTS: , somatic MMR), germline testing, therapeutics and patient advocacy. CONCLUSION: This is the first comprehensive Canadian guideline for LS providing guidance to genetic specialists, laboratories, primary care providers and healthcare providers caring for patients with LS. It is endorsed by the Canadian College of Medical Genetics and the Canadian Association of Genetic Counsellors. The consensus statements are presented as a model for standard of care that improves equitable access to health services for LS across the country. Future work should include a national consensus on LS surveillance, with a goal to harmonise LS care across all provincial and territorial healthcare authorities.
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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 | 0.030 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".