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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.640 | 0.801 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".