APPLaUD: access for patients and participants to individual level uninterpreted genomic data
Bibliographic record
Abstract
BACKGROUND: There is a growing support for the stance that patients and research participants should have better and easier access to their raw (uninterpreted) genomic sequence data in both clinical and research contexts. MAIN BODY: We review legal frameworks and literature on the benefits, risks, and practical barriers of providing individuals access to their data. We also survey genomic sequencing initiatives that provide or plan to provide individual access. Many patients and research participants expect to be able to access their health and genomic data. Individuals have a legal right to access their genomic data in some countries and contexts. Moreover, increasing numbers of participatory research projects, direct-to-consumer genetic testing companies, and now major national sequencing initiatives grant individuals access to their genomic sequence data upon request. CONCLUSION: Drawing on current practice and regulatory analysis, we outline legal, ethical, and practical guidance for genomic sequencing initiatives seeking to offer interested patients and participants access to their raw genomic data.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".