A novel approach to offering additional genomic findings—A protocol to test a two‐step approach in the healthcare system
Bibliographic record
Abstract
Internationally, the practice of offering additional findings (AFs) when undertaking a clinically indicated genomic test differs. In the USA, the recommendation is to include analysis for AFs alongside diagnostic analysis, unless a patient opts-out, whereas European and Canadian guidelines recommend opt-in models. These guidelines all consider the offer of AFs as an activity concurrent with the offer of diagnostic testing. This paper describes a novel two-step model for managing AFs within the healthcare system in Victoria, Australia and presents the study protocol for its evaluation. Adults who have received results of diagnostic whole exome sequencing undertaken within the healthcare system are invited to attend a genetic counseling appointment to consider reanalysis of their stored genomic data for AFs. The evaluation protocol addresses uptake, decision-making, understanding, counseling challenges, and explores preferences for future models of care. Recruitment commenced in November 2017 and will cease when 200 participants have been approached. When the study is concluded, the evaluation results will contribute to the evidence base guiding approaches to counseling and models of care for AFs.
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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 | 0.132 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".