Ontario Cancer Research Ethics Board: Lessons Learned From Developing a Multicenter Regional Institutional Review Board
Bibliographic record
Abstract
PURPOSE: We describe issues and outcomes in the development of a specialized, central institutional review board (IRB) for multicenter oncology protocols. Numerous authoritative bodies have called for a change to the ethics review system to better manage multicenter trials in terms of quality, timeliness, and efficiency. In 2003, the American Society of Clinical Oncology proposed a network of regional IRBs for cancer. Previous experience with central IRBs has been met with mixed success. METHODS: We took a bottom-up approach to organizing a province-wide IRB, which was led by an IRB chair and a clinical investigator at one cancer center. Participation on the part of institutions was voluntary. RESULTS: Uptake in the first 2 years was modest and increased from 11 clinical trials in year 1 to 21 in year 2. In the third year, there was an apparent upsurge in the number of involved centers (14) and in the number of submitted clinical protocols (54). CONCLUSION: Sponsors and investigators are loath to risk development of a novel IRB until there is a clear demonstration of quality, efficiency, and timeliness of decision. Development of a regional, specialized IRB requires considerable efforts to develop and maintain the trust of sponsors, investigators, and institutions despite prior demands for more efficient and timely ethics review. Voluntary institutional participation, clear delineation of roles and responsibilities, and effective execution promote development of this trust.
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 | 0.346 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.007 | 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; 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".