Reporting practices for unsolicited and secondary findings from next‐generation sequencing technologies: Perspectives of laboratory personnel
Bibliographic record
Abstract
While next-generation sequencing (NGS) has enormous potential to identify genetic causes of disease, the nature of the technology means that it can also identify additional information about the individual receiving sequencing that is unrelated to the original rationale for testing. Reporting these unsolicited findings (UF) to clinicians, and subsequently to patients, could lead to potentially lifesaving interventions. Most international guidelines provide limited specific recommendations as to whether these UF should be reported. Little research has been conducted exploring which of these variants are reported in practice. Twenty-six interviews were conducted with 27 laboratory personnel, representing 24 laboratories in Europe (12), Canada (five), and Australasia (Seven) to explore their reporting practices. There is considerable variation between laboratories in the reporting of UF. While some limit their reporting to findings that are relevant to the clinical question, others report UF to varying degrees. In addition, most laboratory personnel interviewed said that their laboratories do not actively search for secondary findings in disease-causing genes unrelated to the clinical question, such as those suggested by the American College of Medical Genetics and Genomics. Our study highlights that laboratories are still grappling with decisions about which UF to report from NGS and are calling for more guidance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".