Implications of generating genetic test results for colon cancer in the international, population-based colon cancer family registry.
Bibliographic record
Abstract
3567 Background: The ability to genotype large numbers of people rapidly and inexpensively for research purposes highlights the need to develop guidelines for providing medically-relevant research results - including unanticipated findings - to study participants. The Colon Cancer Family Registry (C-CFR) is the oldest and largest international colon cancer population-based registry; its experience managing genetic research findings can offer guidance to clinicians and researchers. The C-CFR has enrolled 10,019 cases with colon cancer and 24,708 family members in six registries in the US, Canada, Australia, and New Zealand. Deleterious (“high risk”) germline mutations have been identified in DNA mismatch repair (MMR) genes (MLH1, MSH2, MSH6, PMS2) and the MutYH gene. The aims of this presentation are to: (1) report the uptake of genetic test results by C-CFR participants; (2) systematically compare disclosure protocols and barriers to uptake by registry; (3) make recommendations to guide clinicians and researchers. Methods: Uptake of genetic test results was calculated from data collected by the C-CFR; key investigators (KIs) from each registry completed a survey about disclosure decision-making; KIs also took part in discussions to generate recommendations. Results: Registry-wide molecular testing has identified deleterious MMR germline mutations for at least one member of 424 families (4%) and 48 biallelic MutYH gene carriers. Uptake of test results ranged from 56-86% (n= 1542) across registries. Barriers to disclosure include: (1) lack of pre-existing notification protocols; (2) logistics of re-consent; (3) limited involvement of genetic counselors at some registries; (4) in the US, the requirement that genetic testing be performed in a CLIA approved laboratory; (5) IRBs declining approval; and (6) budget constraints. Conclusions: Based on our international registry’s findings we recommend that researchers generating genetic information establish plans for disclosure at the outset; obtain subject consent a priori; consider subject knowledge and disclosure preferences; provide guidance and budget for clinical follow-up; and involve genetic counselors.
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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 | 0.120 | 0.399 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".