Multisite Research Ethics Review: Problems and Potential Solutions
Bibliographic record
Abstract
Large scale, multisite clinical research trials have been increasing in frequency. As it stands currently, a research project performed at multiple institutions requires ethics review at each institution. While local (institutional) review may be necessary in some instances, repetitive reviews may require unnecessary changes and not serve to further protect participants. Multiple ethics reviews of a single study have been shown to delay research and require, in some cases, significant resources in order to fulfill the requests of individual ethics boards. This literature review discusses the conceptual issues and outlines empirical research surrounding multisite ethics review from different jurisdictions, as well as alternative methods to streamline the ethics review process including reciprocal review, centralized review, and a proposed modification to the centralized review process.
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 | 0.581 | 0.697 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.017 | 0.037 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".