The Impact of COVID-19 on Academic Cancer Clinical Trials in Canada and the Initial Response from Cancer Centers
Bibliographic record
Abstract
The COVID-19 pandemic resulted in temporary holds placed on new trial startups, patient recruitment and follow up visits for trials which contributed to major disruptions in cancer center trial unit operations. To assess the impact, the Canadian Cancer Clinical Trials Network (3CTN) members participated in regional meetings and a survey to understand the impact of the pandemic to academic cancer clinical trials (ACCT) activity, cancer trial unit operations and supports needed for post-pandemic recovery. Trial performance and recruitment data collected from 1 April 2020-31 March 2021 was compared to the same period in previous years. From 1 April-30 June 2020, patient recruitment decreased by 67.5% and trial site activations decreased by 81% compared to the same period in 2019. Recovery to reopening and recruitment of ACCTs began after three months, which was faster than initially projected. However, ongoing COVID-19 impacts on trial unit staffing and operations continue to contribute to delayed trial activations, lower patient recruitment and may further strain centers' capacity for participation in academic-sponsored trials.
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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 | 0.029 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".