Access and use of human tissues from the developing world: ethical challenges and a way forward using a tissue trust
Bibliographic record
Abstract
BACKGROUND: Scientists engaged in global health research are increasingly faced with barriers to access and use of human tissues from the developing world communities where much of their research is targeted. In part, the problem can be traced to distrust of researchers from affluent countries, given the history of 'scientific-imperialism' and 'biocolonialism' reflected in past well publicized cases of exploitation of research participants from low to middle income countries. DISCUSSION: To a considerable extent, the failure to adequately engage host communities, the opacity of informed consent, and the lack of fair benefit-sharing have played a significant role in eroding trust. These ethical considerations are central to biomedical research in low to middle income countries and failure to attend to them can inadvertently contribute to exploitation and erode trust. A 'tissue trust' may be a plausible means for enabling access to human tissues for research in a manner that is responsive to the ethical challenges considered. SUMMARY: Preventing exploitation and restoring trust while simultaneously promoting global health research calls for innovative approaches to human tissues research. A tissue trust can reduce the risk of exploitation and promote host capacity as a key benefit.
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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 | 0.106 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".