Factors Affecting Workload of Cancer Clinical Trials: Results of a Multicenter Study of the National Cancer Institute of Canada Clinical Trials Group
Bibliographic record
Abstract
PURPOSE: Increasingly, cancer treatment centers need to be able to estimate specific costs and resources associated with clinical trials. Because the time requirements of trial coordination and data collection are not well known, the Clinical Research Associates (CRA) Committee of the National Cancer Institute of Canada Clinical Trials Group carried out a multicenter study to measure trials’ task times and evaluate the effects of certain factors. METHODS: A data collection instrument was designed and validated before its implementation in the study. Eighty-three CRAs from 24 cancer treatment institutions across Canada collected timing observations of 41 tasks (156 subtasks). Information from all stages of trials activity (protocol management, eligibility and entry, treatment, and follow-up and final stage) was obtained, from initial negotiations to follow-up after study closure. RESULTS: After controlling for stage, phase and sponsor were found to be significant independent factors. Analysis within the stages showed similar patterns. New drug inclusion as a factor was confounded with phase. Industry-sponsored studies had significantly higher overall mean times than did local and cooperative group studies. Early-phase studies required more time than did phase III trials. External sponsorship of any kind increased CRA time more than that necessary for locally coordinated studies, except during the protocol management stage. The burden of a phase I study increased to greater than average once underway and accruing patients. CONCLUSION: Our data demonstrated that sponsor and study phase are important factors to be taken into consideration when estimating clinical trial costs and resource use.
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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 | 0.068 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".