Partnering with patients to deliver window-of-opportunity clinical trials in immuno-oncology.
Bibliographic record
Abstract
e23023 Background: Oncologic drug development is costly and protracted, often exceeding $2 billion USD and spanning over a decade. Window-of-Opportunity (WOO) clinical trials offer a novel, time-efficient approach for evaluating therapeutic strategies in treatment-naïve or recurrent cancer patients. Despite their potential, WOO trials face barriers such as low patient accrual and challenges associated with engaging early-stage cancer patients. We propose a structured patient partner (PP) model to enhance the design and execution of WOO trials, aimed at addressing accrual barriers and improving trial relevance and feasibility for trial participants. Methods: The Ontario Institute for Cancer Research (OICR) WOO Network implemented a five-step framework—acknowledge, appoint, apply, assess, and adjust—to facilitate meaningful PP engagement in WOO trials. This approach involved identifying and appointing PPs to specific trials aligned with their lived experience and expertise, providing comprehensive onboarding and training, and integrating their feedback into key trial components, including protocols, informed consent documents, and recruitment strategies. Patient engagement was evaluated annually over a three-year period (2022–2025) using the validated Patient Engagement in Research Scale (PEIRS-22). Results: Over three years, ten PPs contributed to nine WOO trials with 6, 7 and 6 PIERS assessments reported in 2022-2024 respectively. Each year, the PIERS feedback was used to determine refinements to improve the PP model. The median (IQR) overall engagement score was 87.5% (79.5%-95.5%), 85.2% (80.7%-97.7%) and 93.2% (79.8%-99.7%) by year respectively. Median scores across each of the 7 domains remained relatively constant across all three years, with the largest absolute improvement occurring in the general experience domain (22.5 to 25.5) and team environment domain (6.5 to 8.0), and no domain decreasing by more than 0.5 points. Conclusions: Meaningful engagement of PPs in clinical trials requires a deliberate, structured approach that integrates them at critical stages of the trial lifecycle, combined with robust evaluation mechanisms to identify areas for improvement to drive continuous refinement. Future efforts will prioritize careful evaluation of the impact of successful PP engagement on improving trial accrual outcomes.
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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 | 0.090 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".