Incidental Findings in Data-Intensive Postgenomics Science and Legal Liability of Clinician–Researchers: Ready for Vaccinomics?
Bibliographic record
Abstract
Vaccinomics encompasses a host of multiomics approaches to characterize variability in host-environment (including pathogens) interactions, with a view to a more directed or personalized use of vaccine-based health interventions. Although vaccinomics has the potential to reduce adverse effects and increase efficacy of vaccines, the use of high-throughput, data-intensive technologies may also lead to unanticipated discoveries beyond the initial aims of a vaccinomics study--discoveries that could be highly significant to the health of the research participants. How do clinician-researchers faced with such information have to act? What are the attendant legal duties in such circumstances and how do they differ from the duties of non-clinician researchers? Together with a critical analysis of the international laws and policies framing researchers' duties with regard to incidental findings, this article also draws from Quebec's civil law--with its rich jurisprudence on clinician and researcher liability--as a case study to evaluate the potential legal implications associated with vaccinomics investigations. Given previous lessons learned from other data-intensive sciences, the education of clinician-researchers with regard to their roles, limitations, and legal obligations remains an important strategy to prevent potential legal complications and civil liability in vaccinomics research in the postgenomics era.
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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 | 0.266 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.079 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.031 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".