Genome Screening, Reporting and Counseling for Healthy Populations
Bibliographic record
Abstract
Introduction : Rapid advancements of genome sequencing (GS) technologies have enhanced our understanding of the relationship between genes and human disease. In order to incorporate genomic information into the practice of medicine, new processes for the analysis, reporting and communication of GS data are needed. Methods : blood samples were collected from adults with a PCR-confirmed SARS-CoV-2 (COVID-19) diagnosis (target N=1500). GS was performed. Data was filtered and analyzed using custom pipelines and gene panels. We developed unique patient-facing materials, including an online intake survey, group counseling presentation, and consultation letters in addition to a comprehensive GS report. Results : The final report includes results generated from GS data: 1) Monogenic disease risks; 2) Carrier status; 3) Pharmacogenomic variants; 4) Polygenic risk scores for common conditions; 5) HLA genotype; 6) Genetic ancestry; 7) Blood group; and, 8) COVID-19 viral lineage. Participants complete pre-test genetic counseling and confirm preferences for secondary findings before receiving results. Counseling and referrals are initiated for clinically significant findings. Conclusion : We developed a genetic counseling, reporting, and return of results framework that integrates GS information across multiple areas of human health, presenting possibilities for the clinical application of comprehensive GS data in healthy individuals.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".