Online Module for Carrier Screening in Ashkenazi Jewish Individuals Compared with In‐Person Genetics Education: A Randomized Controlled Trial
Bibliographic record
Abstract
To increase accessibility to genetics services for low-urgency patients seeking Ashkenazi Jewish (AJ) carrier screening, we designed an interactive computer (IC) module that provides pre-test genetics education and allows genetics professionals to order the test without meeting the patients beforehand. We compared this module with in-person genetic counseling (GC) using a randomized trial. AJ individuals were randomized to undergo genetics education via the IC module (n = 26) or GC (n = 28). We compared post-interventional genetics knowledge, perceived genetic risk, and anxiety between the two groups, after accounting for pre-interventional scores, using ANCOVA. Wilcoxon Rank-Sum test was used to compare post-interventional satisfaction. Post-interventional genetics knowledge, risk perception, or anxiety were not significantly different between the two groups after accounting for baseline scores (p = 0.50-0.54), although the data are inconclusive regarding the module's non-inferiority at a 5% margin. Post-intervention satisfaction scores were generally higher in the GC group than the IC module group. Our IC module has the potential to improve access to clinical genetics services for patients and staff, but it is not suitable for all AJ patients and cannot completely replace the benefits of in-person consultations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".