Putting Patients First: Pragmatic Trials in Gynecologic Oncology
Bibliographic record
Abstract
In November 2024, the Society of Gynecologic Oncology of Canada hosted a 2-day, interdisciplinary Pragmatic Clinical Trials (PCTs) Workshop with the goal of launching an initiative to develop and promote PCTs within the Canadian gynecologic oncology research environment. The programme brought together multiple stakeholders, including patients with ovarian cancer, patient advocates, experts in PCTs, gynecologic oncologists, medical oncologists and clinical fellows. Foundational elements of pragmatism were emphasized in the context of the primary goal of PCTs, showing the real-world effectiveness of interventions in broad patient groups. Examples of how PCT outcomes can inform and influence clinical decision making and health policy were presented in the context of those outcomes that matter most to patients with cancer. The patients and patient advocates had the essential role of helping clinical investigators co-design PCT protocols to answer common, important, and practical questions that focus on outcomes that matter to patients. These endpoints included overall survival, quality of life and promotion of informed patient decision making. Tangible workshop outcomes included the development of several new proposals for PCTs inspirited and directed by the patient voice. Further educational initiatives to engage clinical gynecologic oncology investigators at all stages in their career are being planned.
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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 | 0.409 | 0.563 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".