Reporting genetic results in research studies: Summary and recommendations of an NHLBI working group
Bibliographic record
Abstract
Prospective epidemiologic studies aid in identifying genetic variants associated with diseases, health risks, and physiologic traits. These genetic variants may eventually be measured clinically for purposes of diagnosis, prognosis, and treatment. As evidence of the potential clinical value of such information accrues, research studies face growing pressure to report these results to study participants or their physicians, even before sufficient evidence is available to support widespread screening of asymptomatic persons. There is thus a need to begin to develop consensus on whether and when genetic findings should be reported to participants in research studies. The National Heart, Lung, and Blood Institute (NHLBI) convened a Working Group on Reporting Genetic Results in Research Studies to discuss if, when, and how genetic information should be reported to study participants. The Working Group concluded that genetic test results should be reported to study participants when the associated risk for the disease is significant; the disease has important health implications such as premature death or substantial morbidity or has significant reproductive implications; and proven therapeutic or preventive interventions are available. Finally, the Working Group recommended procedures for reporting genetic research results and encouraged increased efforts to create uniform guidelines for this activity.
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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 | 0.354 | 0.445 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.019 | 0.009 |
| Research integrity | 0.035 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".