Time required to initiate a clinical trial in Canada at the onset of the COVID-19 pandemic: an observational research-in-motion study
Bibliographic record
Abstract
BACKGROUND: Randomized controlled trials (RCTs) provide essential evidence to inform practice, but the many necessary steps result in lengthy times to initiation, which is problematic in the case of rapidly emerging infections such as COVID-19. This study aimed to describe the start-up timelines for the Canadian Treatments for COVID-19 (CATCO) RCT. METHODS: We surveyed hospitals participating in CATCO and ethics submission sites using a structured data abstraction form. We measured durations from protocol receipt to site activation and to first patient enrolment, as well as durations of administrative processes, including research ethics board (REB) approval, contract execution and lead times between approvals to site activation. RESULTS: All 48 hospitals (26 academic, 22 community) and 4 ethics submission sites responded. The median time from protocol receipt to trial initiation was 111 days (interquartile range [IQR] 39-189 d, range 15-412 d). The median time between protocol receipt and REB submission was 41 days (IQR 10-56 d, range 4-195 d), from REB submission to approval, 4.5 days (IQR 1-12 d, range 0-169 d), from REB approval to site activation, 35 days (IQR 22-103 d, range 0-169 d), from protocol receipt to contract submission, 42 days (IQR 20-51 d, range 4-237 d), from contract submission to full contract execution, 24 days (IQR 15-58 d, range 5-164 d) and from contract execution to site activation, 10 days (IQR 6-27 d, range 0-216 d). Processes took longer in community hospitals than in academic hospitals. INTERPRETATION: The time required to initiate RCTs in Canada was lengthy and varied among sites. Adoption of template clinical trial agreements, greater harmonization or central coordination of ethics submissions, and long-term funding of platform trials that engage academic and community hospitals are potential solutions to improve trial start-up efficiency.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".