Optimizing the design of pragmatic trials: key issues remain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clinical trials have largely focused on whether an intervention can work. To ensure valid and powerful testing of this hypothesis, trials attempt to maximize the effect of the intervention of interest, controlling other factors that can confound comparisons. The benefits observed in these studies are often not sustained once the treatment is used in routine care, leaving regulators, practitioners and patients with a paucity of reliable evidence to assist decision-making. Attempts to address this need have led to 'pragmatic trials' that prioritize applicability of findings to real-world practice by minimizing design features that produce less pertinent information. Minimizing biases in this pragmatic context remains a very difficult task, however. This paper reviews some of these challenges and highlights specific aspects of design that must be approached with a pragmatic attitude.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.430 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it