Expect the unexpected: screening for secondary findings in clinical genomics research
Bibliographic record
Abstract
Background: Due to decreasing cost, and increasing speed and precision, genomic sequencing in research is resulting in the generation of vast amounts of genetic data. The question of how to manage that information has been an area of significant debate. In particular, there has been much discussion around the issue of 'secondary findings' (SF)-findings unrelated to the research that have diagnostic significance. Sources of data: The following includes ethical commentaries, guidelines and policies in respect to large-scale clinical genomics studies. Areas of agreement: Research participant autonomy and their informed consent are paramount-policies around SF must be made clear and participants must have the choice as to which results they wish to receive, if any. Areas of controversy: While many agree that clinically 'actionable' findings should be returned, some question whether they should be actively sought within a research protocol. Growing points: SF present challenges to a growing field; diverse policies around their management have the potential to hinder collaboration and future research. Areas timely for developing research: The impact of returning SF and accurate estimates of their clinical utility are needed to inform future protocol design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".