Assessing an Interactive Online Tool to Support Parents' Genomic Testing Decisions
Bibliographic record
Abstract
Clinical use of genome-wide sequencing (GWS) requires pre-test genetic counseling, but the availability of genetic counseling is limited. We developed an interactive online decision-support tool, DECIDE, to make genetic counseling, patient education, and decision support more readily available. We performed a non-inferiority trial comparing DECIDE to standard genetic counseling to assess the clinical value of DECIDE for pre-GWS counseling. One hundred and six parents considering GWS for their children with epilepsy were randomized to conventional genetic counseling or DECIDE. Following the intervention, we measured parents' knowledge and empowerment and asked their opinions about using DECIDE. Both DECIDE and conventional genetic counseling significantly increased parents' knowledge, with no difference between groups. Empowerment also increased but by less than 2% in each group. Parents liked using DECIDE and found it useful; 81% would recommend it to others; 49% wished to use it along with a genetic counselor; 26% of parents preferred to see a genetic counselor; 7% preferred DECIDE alone; and 18% had no preference. DECIDE appears equivalent to genetic counseling at conveying information. In addition, it was highly acceptable to the majority of study participants, many of whom indicated that it was useful to their decision-making. Use of DECIDE as a pre-test tool may extend genetic counseling resources.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".