A personalized genomic results e-booklet, co-designed and pilot-tested by families
Bibliographic record
Abstract
Objective: To develop and evaluate a personalizable genomic results e-booklet that helps families understand their genomic testing results and navigate available resources. Methods: The need for the Genomics Results e-Booklet was identified by families, after which this tool was developed by a team of clinical researchers and three parent-advisors. We customized the genomic results e-booklet for 50 families participating in a genomic sequencing research study. We conducted an assessment using a 19-question survey and semi-structured interviews to elicit feedback and iteratively improve the tool. Results: 25 users provided feedback via questionnaires and seven respondents were interviewed. Genomic Results e-Booklet recipients responded favorably: 96% of participants stated that it helped them remember information shared during their results appointment, 80% said it had or would help them communicate their results with other healthcare providers, 68% felt that it helped to identify and guide their next steps, and 72% anticipated that the e-booklet would have future utility. Conclusion: The Genomic Results e-Booklet is a patient and family-oriented resource that complements post-test genetic counselling. Innovation: Compared to traditional laboratory reports and clinical letters, the Genomics Results e-Booklet is patient-conceived and patient-centered, and allows clinicians to efficiently personalize content and prioritize patient understanding and support.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".