Pragmatic Clinical Trials, Linkages and Agreements: Interjurisdictional Collaboration on Pragmatic Clinical Trials
Bibliographic record
Abstract
The randomized clinical trial framework has proven to be a robust way of evaluating and comparing medications and procedures for many years. The promise of measured and unmeasured balance and equipoise through randomization remains very appealing. Emerging challenges for trialists have been diminishing returns in terms of effect sizes as treatments become more effective and the ever-growing costs associated with the operation of clinical trials. The costs and operational overhead limit the ability of publicly funded trials to operate at a sufficiently robust level. Recently we have been working to engage clinicians and patients in pragmatic clinical trials which in part leverage routinely collected health system data to both provide an unbiased and effective means of obtaining high quality data for recruiting, randomization and outcomes. As these trials have expanded between provincial jurisdictions, we have had to look for creative solutions to assembling and sharing data in order to reach the trial goals. This presentation will examine some of the technical and logistical challenges that we have faced, how they have been addressed and the path forward for clinical 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 | 0.606 | 0.691 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.032 | 0.044 |
| Open science | 0.008 | 0.054 |
| Research integrity | 0.024 | 0.029 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".