Continuous monitoring of clinical research: the REB's unfulfilled obligation
Bibliographic record
Abstract
The lack of specificity about the role of Research Ethics Boards concerning monitoring of clinical research in this new context of private sponsorship, and centrally depending on researchers to monitor clinical research is worrying—given the level of research misconduct and the recurrence of preventable scandals and unethical practices. This thesis is about the ethical necessity of ongoing REB monitoring, while clinical research is being conducted. An initial review, nominal annual reporting, and ad hoc notifications of problems after they occur are likely only to detect problems after harms have occurred. While acknowledging the recommendations of the various policy documents like Canada's TCPS2, US Common Rule, and ICH-GCP have been inadequate, I argue that adequate REB post-initial-review monitoring requires greater REB involvement, rather than trust and researchers’ assurances. The REBs’ monitoring should include continual onsite monitoring and paternalistic continuous review, to protect subjects who are contributing to scientific knowledge. Subject safety and overall research integrity are imperative to good science.
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 | 0.243 | 0.378 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.014 | 0.042 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".