Bridging consent: from toll bridges to lift bridges?
Bibliographic record
Abstract
BACKGROUND: The ability to share human biological samples, associated data and results across disease-specific and population-based human research biobanks is becoming increasingly important for research into disease development and translation. Although informed consent often does not anticipate such cross-domain sharing, it is important to examine its plausibility. The purpose of this study was to explore the feasibility of bridging consent between disease-specific and population-based research. Comparative analyses of 1) current ethical and legal frameworks governing consent and 2) informed consent models found in disease-specific and population-based research were conducted. DISCUSSION: Ethical and legal frameworks governing consent dissuade cross-domain data sharing. Paradoxically, analysis of consent models for disease-specific and population-based research reveals such a high degree of similarity that bridging consent could be possible if additional information regarding bridging was incorporated into consent forms. We submit that bridging of consent could be supported if current trends endorsing a new interpretation of consent are adopted. To illustrate this we sketch potential bridging consent scenarios. SUMMARY: A bridging consent, respectful of the spirit of initial consent, is feasible and would require only small changes to the content of consents currently being used. Under a bridging consent approach, the initial data and samples collection can serve an identified research project as well as contribute to the creation of a resource for a range of other projects.
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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 | 0.222 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.011 | 0.031 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".