Ethics Governance Outside the Box: Reimagining Blockchain as a Policy Tool to Facilitate Single Ethics Review and Data Sharing for the 'omics' Sciences
Bibliographic record
Abstract
Clinical research and health information data sharing are but ripples in a growing wave of reimagined applications of distributed ledger technologies beyond the digital marketplace for which they were originally created. This paper explores the use of distributed ledger technologies to facilitate single institutional ethics review of multi-site, collaborative studies in the dataintensive sciences such as genetics and genomics. Immutable record-keeping, automatable protocol amendments and direct connectivity between stakeholders in the research enterprise (e.g., researchers, research ethics committees, institutions, funders and regulators) comprise several of the conceptual and technological advantages of distributed ledger technologies to research ethics review. This novel-use proposal dovetails recent policy reforms to research ethics review across North America that mandate a single ethics review for any study that takes place across more than one research site. Such reforms in the United States, Canada and Australia replace prior institution-by-institution approval mechanisms that contributed to significant research delays and duplicative procedures for collaborative research worldwide. While this paper centers on the Common Rule revision in the United States, the single ethics review mandate is a noteworthy example of regulation evolving in parallel with advances in the dataintensive sciences it governs. The informational exchange capacities of distributed ledger technologies align well with the procedural goals of streamlining the ethics review system under the new Common Rule ahead of its official implementation on January 19, 2020. The ethical, legal and social implications of applying such technologies to ethics review will be explored in this concept paper. Namely, the paper proposes how administrative data from research ethics committees (REC) could be protected and shared responsibly, as well as interinstitutional cooperation negotiated within a centralized network of research ethics committees using the blockchain. Keywords: Blockchain, Data Sharing, Ethics Review, Governance, IRB, Research, Single Mutual Recognition
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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 | 0.165 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.023 | 0.041 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".