A model for the return and referral of all clinically significant secondary findings of genomic sequencing
Bibliographic record
Abstract
Secondary findings (SFs) identified through genomic sequencing (GS) can offer a wide range of health benefits to patients. Resource and capacity constraints pose a challenge to their clinical management; therefore, clinical workflows are needed to optimise the health benefits of SFs. In this paper, we describe a model we created for the return and referral of all clinically significant SFs, beyond medically actionable results, from GS. As part of a randomised controlled trial evaluating the outcomes and costs of disclosing all clinically significant SFs from GS, we consulted genetics and primary care experts to determine a feasible workflow to manage SFs. Consensus was sought to determine appropriate clinical recommendations for each category of SF and which clinician specialist would provide follow-up care. We developed a communication and referral plan for each category of SFs. This involved referrals to specialised clinics, such as an Adult Genetics clinic, for highly penetrant medically actionable findings. Common and non-urgent SFs, such as pharmacogenomics and carrier status results for non-family planning participants, were directed back to the family physician (FP). SF results and recommendations were communicated directly to participants to respect autonomy and to their FPs to support follow-up of SFs. We describe a model for the return and referral of all clinically significant SFs to facilitate the utility of GS and promote the health benefits of SFs. This may serve as a model for others returning GS results transitioning participants from research to clinical settings.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".