Opportunistic screening for broad range of medically relevant secondary findings: Laboratory benefits and burdens
Bibliographic record
Abstract
PURPOSE: Exome and genome sequencing enable opportunistic screening for secondary findings (SFs). We report on exome analysis for a broad range of medically relevant SFs in the setting of the Incidental Genomics randomized clinical trial (NCT03597165). METHODS: Participants had exome sequencing and were randomized to receive only primary cancer findings (control) or cancer findings and a choice of SFs (intervention). RESULTS: Across 279 participants, there were 4441 unique variants in SF genes: 5.0% (221) were reportable pathogenic/likely pathogenic variants, and 81.4% (3615) were nonreportable variants of uncertain significance (VUS). Intervention arm participants had on average 2.6 (SD 1.66, range 0-9) pathogenic/likely pathogenic variants and 29.5 VUS (SD 13.2, range 2-74). SFs for monogenic disease risk were reported in 35.3% (49/139) of participants (American College of Medical Genetics and Genomics non-cancer subset in 1.4%) and carrier status in 89.3% (117/131). In the intervention arm, variant filtration was 7.7 times longer per case (95% CI 5.3 to 11.3, P < .0001), variant classification was 13.3 times longer (95% CI 10.6 to 16.5, P < .0001), and report preparation was 3.3 times longer (95% CI 2.6 to 4.1, P < .0001). CONCLUSION: Although the yield of reportable SFs was high, this was accompanied by many nonreportable VUS and increased efforts for exome analysis.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".