The ethical, legal and social implications of umbilical cord blood banking: learning important lessons from the protection of human genetic information.
Bibliographic record
Abstract
Internationally networked umbilical cord blood banks hold great promise for better clinical outcomes, but also raise a host of potential ethical and legal concerns. There is now significant accumulated experience in Australia and overseas with regard to the establishment of human genetic research databases and tissue collections, popularly known as "biobanks". For example, clear lessons emerge from the controversies that surrounded, stalled or derailed the establishment of some early biobanks, such as Iceland's deCODE, Autogen's Tonga database, a proposed biobank in Newfoundland, Canada, and the proposed Taiwan biobank. More recent efforts in the United Kingdom, Japan, Quebec and Tasmania have been relatively more successful in generating public support, recognising the critical need for openness and transparency, and ample public education and debate, in order to build community acceptance and legitimacy. Strong attention must be paid to ensuring that other concerns--about privacy, discrimination, informed consent, governance, security, commercial fairness and financial probity--are addressed in structural terms and monitored thereafter, in order to maintain public confidence and avoid a backlash that inevitably would imperil such research. Once lost, credibility is very difficult to restore.
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 | 0.032 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.017 |
| 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".