DECIDE: a Decision Support Tool to Facilitate Parents’ Choices Regarding Genome‐Wide Sequencing
Bibliographic record
Abstract
We describe the rationale, development, and usability testing for an integrated e-learning tool and decision aid for parents facing decisions about genome-wide sequencing (GWS) for their children with a suspected genetic condition. The online tool, DECIDE, is designed to provide decision-support and to promote high quality decisions about undergoing GWS with or without return of optional incidental finding results. DECIDE works by integrating educational material with decision aids. Users may tailor their learning by controlling both the amount of information and its format - text and diagrams and/or short videos. The decision aid guides users to weigh the importance of various relevant factors in their own lives and circumstances. After considering the pros and cons of GWS and return of incidental findings, DECIDE summarizes the user's responses and apparent preferred choices. In a usability study of 16 parents who had already chosen GWS after conventional genetic counselling, all participants found DECIDE to be helpful. Many would have been satisfied to use it alone to guide their GWS decisions, but most would prefer to have the option of consulting a health care professional as well to aid their decision. Further testing is necessary to establish the effectiveness of using DECIDE as an adjunct to or instead of conventional pre-test genetic counselling for clinical genome-wide sequencing.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".