The role for pragmatic randomized controlled trials (pRCTs) in comparative effectiveness research
Bibliographic record
Abstract
There is a growing appreciation that our current approach to clinical research leaves important gaps in evidence from the perspective of patients, clinicians, and payers wishing to make evidence-based clinical and health policy decisions. This has been a major driver in the rapid increase in interest in comparative effectiveness research (CER), which aims to compare the benefits, risks, and sometimes costs of alternative health-care interventions in 'the real world'. While a broad range of experimental and nonexperimental methods will be used in conducting CER studies, many important questions are likely to require experimental approaches - that is, randomized controlled trials (RCTs). Concerns about the generalizability, feasibility, and cost of RCTs have been frequently articulated in CER method discussions. Pragmatic RCTs (or 'pRCTs') are intended to maintain the internal validity of RCTs while being designed and implemented in ways that would better address the demand for evidence about real-world risks and benefits for informing clinical and health policy decisions. While the level of interest and activity in conducting pRCTs is increasing, many challenges remain for their routine use. This article discusses those challenges and offers some potential ways forward.
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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 | 0.854 | 0.919 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.009 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.025 | 0.043 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.033 | 0.039 |
| Insufficient payload (model declined to judge) | 0.012 | 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".