Taking tissue seriously means taking communities seriously
Bibliographic record
Abstract
BACKGROUND: Health research is increasingly being conducted on a global scale, particularly in the developing world to address leading causes of morbidity and mortality. While research interest has increased, building scientific capacity in the developing world has not kept pace. This often leads to the export of human tissue (defined broadly) from the developing to the developed world for analysis. These practices raise a number of important ethical issues that require attention. DISCUSSION: In the developed world, there is great heterogeneity of regulatory practices regarding human tissues. In this paper, we outline the salient ethical issues raised by tissue exportation, review the current ethical guidelines and norms, review the literature on what is known empirically about perceptions and practices with respect to tissue exportation from the developing to the developed world, set out what needs to be known in terms of a research agenda, and outline what needs to be done immediately in terms of setting best practices. We argue that the current status of tissue exportation is ambiguous and requires clarification lest problems that have plagued the developed world occur in the context of global heath research with attendant worsening of inequities. Central to solutions to current ethical concerns entail moving beyond concern with individual level consent and embracing a robust interaction with communities engaged in research. CONCLUSION: Greater attention to community engagement is required to understand the diverse issues associated with tissue exportation.
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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 | 0.046 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.076 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".