Ontario Protocol Assessment Level: Clinical Trial Complexity Rating Tool for Workload Planning in Oncology Clinical Trials
Bibliographic record
Abstract
PURPOSE: The Ontario Institute for Cancer Research supported the creation of a working group with the objective of developing a standard rating scale to evaluate clinical trial complexity and applying the scale to facilitate workload measurement for Ontario cancer research sites. METHODS: The lack of a mechanism to measure the workload involved in a clinical trials protocol was identified and confirmed by a literature review. To collect information on how Ontario sites were assessing workload, a survey was distributed and evaluated. As a result, the working group developed the Ontario Protocol Assessment Level (OPAL), a protocol complexity rating scale designed to capture the workload involved in a clinical trial. After a training workshop on the application, OPAL was evaluated by 17 Ontario cancer centers to demonstrate its reliability and consistency during a 3-month pilot study. RESULTS: Twenty-seven protocols were reviewed by multiple sites, and the majority of the sites reported OPAL score differences between 0 and 1.5. CONCLUSION: OPAL provides clinical trials departments with an objective method of quantifying clinical trials activity on the basis of study protocol complexity. With consistent application of OPAL, sites can manage staffing objectively. The working group is continuing to monitor the application of OPAL in Ontario.
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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 | 0.145 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".