Evaluation of a decision aid for incidental genomic results, the Genomics ADvISER: protocol for a mixed methods randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Genome sequencing, a novel genetic diagnostic technology that analyses the billions of base pairs of DNA, promises to optimise healthcare through personalised diagnosis and treatment. However, implementation of genome sequencing faces challenges including the lack of consensus on disclosure of incidental results, gene changes unrelated to the disease under investigation, but of potential clinical significance to the patient and their provider. Current recommendations encourage clinicians to return medically actionable incidental results and stress the importance of education and informed consent. Given the shortage of genetics professionals and genomics expertise among healthcare providers, decision aids (DAs) can help fill a critical gap in the clinical delivery of genome sequencing. We aim to assess the effectiveness of an interactive DA developed for selection of incidental results. METHODS AND ANALYSIS: We will compare the DA in combination with a brief Q&A session with a genetic counsellor to genetic counselling alone in a mixed-methods randomised controlled trial. Patients who received negative standard cancer genetic results for their personal and family history of cancer and are thus eligible for sequencing will be recruited from cancer genetics clinics in Toronto. Our primary outcome is decisional conflict. Secondary outcomes are knowledge, satisfaction, preparation for decision-making, anxiety and length of session with the genetic counsellor. A subset of participants will complete a qualitative interview about preferences for incidental results. ETHICS AND DISSEMINATION: This study has been approved by research ethics boards of St. Michael's Hospital, Mount Sinai Hospital and Sunnybrook Health Sciences Centre. This research poses no significant risk to participants. This study evaluates the effectiveness of a novel patient-centred tool to support clinical delivery of incidental results. Results will be shared through national and international conferences, and at a stakeholder workshop to develop a consensus statement to optimise implementation of the DA in practice. TRIAL REGISTRATION NUMBER: NCT03244202; Pre-results.
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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 | 0.060 | 0.066 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.099 | 0.014 |
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".