“Deconstructing” Biobank Communication of Results
Bibliographic record
Abstract
Biobanks have been troubled by a history of confusion and controversy around certain key concepts such as “broad consent”, and, more recently, “return of results”. This article analyses the return of results only as it pertains to the participation of (presumably healthy) volunteers in the creation of longitudinal biobank infrastructures for future unspecified research. Limiting ourselves to the trajectory of a typical protocol then that begins with: the arrival of volunteers at assessment centres for the collection of blood and the filling-in of extensive questionnaires on lifestyle, socio-demographic factors and family history; followed by long term storage; and finally the use by researchers accessing such biobanks (it is evident that it is necessary to distinguish between the different obligations that may arise at distinct moments in this trajectory). We posit that there are five types of communication, and we explore the best means of protecting the privacy of those involved in such biobanks, concluding that international policies are converging towards an ethical duty to return individual genetic research results to subjects, provided there is proof of validity, significance and benefit.
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 | 0.203 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.116 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".