A Review of Regulatory Frameworks Governing Biobanking in the Low and Middle Income Member Countries of BCNet
Bibliographic record
Abstract
Biomedical research based on the sharing and use of ever larger volumes of samples and data is increasingly becoming an essential component of scientific discovery. The success of biobanking and genomic research is dependent on the broad sharing of resources for use by investigators. However, important ethical challenges need to be addressed for the sample and data sharing to be successful. Despite low- and middle-income countries (LMICs) carrying a higher burden of disease, biomedical research conducted to date has mainly focused on high-income countries. In order for LMICs to benefit from the advances in such research, normative documents (such as laws and guidelines) play a significant role in allowing LMIC projects to partake and be represented in global biomedical research. The administration and management of the ethical aspects of biobanking, including informed consent, are key components in ensuring that samples and data can legally and ethically be used and shared. As part of its support to the LMIC biobanks, the International Agency for Research on Cancer (IARC) established a biobank and population cohort building network (BCNet) in 2013 with the aims of providing support (including education and training) and facilitating the development and improvement of biobanking infrastructure in LMICs. A comparative analysis of the laws and guidelines in BCNet countries was completed to highlight some of the ethical and legal issues related to biobanking in LMICs and to identify examples of effective systems of governance already in operation.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".