Stem cell banking: between traceability and identifiability
Bibliographic record
Abstract
Stem cell banks are increasingly seen as an essential resource of biological materials for both basic and translational research. Stem cell banks support transnational access to quality-controlled and ethically sourced stem cell lines from different origins and of varying grades. According to the Organisation for Economic Co-operation and Development, advances in regenerative medicine are leading to the development of a bioeconomy, 'a world where biotechnology contributes to a significant share of economic output'. Consequently, stem cell banks are destined to constitute a pillar of the bioeconomy in many countries. While certain ethical and legal concerns are specific to the nature of stem cells, stem cell banking could do well to examine the approaches fostered by tissue banking generally. Indeed, the past decade has seen a move to simplify and harmonize biological tissue and data banking so as to foster international interoperability. In particular, the issues of consent and of traceability illustrate not only commonalities but the opportunity for stem cell banking to appreciate the lessons learned in biobanking generally. This paper analyzes convergence and divergence in issues surrounding policy harmonization, transnational sharing, informed consent, traceability and return of results in the context of stem cell banks.
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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 | 0.128 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.069 |
| Scholarly communication | 0.020 | 0.045 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".