Development of an Interactive Patient Education Tool for Genetic Testing in Autism Spectrum Disorder
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a prevalence of 1 in 68 children. The cause of the disorder is unknown, but research suggests that it involves a combination of genetic and environmental factors. Due to recent advances in sequencing technology, next generation sequencing is being incorporated into clinical practices, enabling researchers to obtain genetic data from ASD patients and elucidate the genetic variants that contribute to the disorder. Ultimately, this may lead to earlier diagnoses and interventions for future generations. The integration of next generation sequencing into clinical practices also benefits patients, because it can be used to identify other fatal genetic disorders associated with ASD and to advise parents on recurrence risk. Despite the benefits of genetic testing in the ASD field, there remain two communication gaps between families of patients affected by ASD and genetic counselors. First, there are few patient education resources on why patients with ASD should get genetic testing, resulting in a lack of awareness. The second communication gap is the challenge of explaining complex genomic results to families of patients who choose to undergo genetic testing. This master’s research project aims to bridge both communication gaps through the implementation of an interactive web-based resource. With the use of visual analogies and storytelling techniques, the tool will aid in knowledge translation and improve society’s understanding of genetic testing in the field of ASD.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.013 |
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".