Ethics application protocols for multicentre clinical studies in Canada: A paediatric rheumatology experience
Bibliographic record
Abstract
INTRODUCTION: Individual institutions govern research ethics applications and each must administer and regulate their own protocols. Variations in ethics review procedures and expectations among centres impose impediments to efficiently conducting multicentre studies. METHODS: Observations relating to preparing multisite ethics documents for a study conducted by Canadian paediatric rheumatology investigators are described. Research ethics applications from the 12 participating centres were compared. RESULTS: Although the applications were similar in their content, they differed in their formatting. All applications shared a commitment to ensuring that the study conformed to exemplary ethical standards. CONCLUSIONS: There is wide variation in the multicentre clinical study ethics application process at the institutional level. Considering the common fundamental elements required by all ethics review boards, the present study conceptualized introducing a discipline-specific uniform ethics application process acceptable to all Canadian research ethics boards. This may be a more efficient strategy that could help lessen the burden of collaborative research.
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 | 0.213 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".