Informed consent in international normative texts and biobanking policies
Bibliographic record
Abstract
With the progress in bioinformatics, genomics, and epidemiology, biobanks, as repositories of populations’ biological samples as well as of personal and medical information, are becoming an essential research tool. Despite the potential benefits biobanks may bring and the options presented by some of the current biobanks’ consent policies, there remain ethical concerns regarding the autonomy and dignity of research participants if consent is not fully informed as dictated in the terms of traditional informed consent. This article aims at providing an overview of the approaches taken by the main international norms with respect to informed and broad consent and how well these norms are integrated by biobanks or biobank consortia. We conclude that broad consent could be an important tool to achieve the benefits of large-scale biobanks projects. If it is to be accepted, its regulation and implementation need to be mindful of the participant’s dignity and autonomy and sensitive to the need for international coherence and harmonization.
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 | 0.209 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".