Reducing the confusion and controversies around pragmatic trials: using the Cardiovascular Health Awareness Program (CHAP) trial as an illustrative example
Bibliographic record
Abstract
UNLABELLED: Knowledge translation (KT) involves implementation of evidence-based strategies and guidelines into practice to improve the process of care and health outcomes for patients. Findings from pragmatic trials may be used in KT to provide patients, healthcare providers and policymakers with information to optimize healthcare decisions based on how a given strategy or intervention performs under the real world conditions. However, pragmatic trials have been criticized for having the following problems: i) high rates of loss to follow-up; ii) nonadherence to study intervention; iii) unblinded treatment and patient self-assessment, which can potentially create bias; iv) being less perfect experiments than efficacy trials; v) sacrificing internal validity to achieve generalizability; and vi) often requiring large sample sizes to detect small treatment effects in heterogeneous populations. In this paper, we discuss whether these criticisms hold merit, or if they are simply driven by confusion about the purpose of pragmatic trials. We use the Cardiovascular Health Awareness Program (CHAP) trial--a community randomized pragmatic trial designed to assess whether offering a highly organized, community-based CHAP intervention compared to usual care can reduce cardiovascular disease-related outcomes--to address these specific criticisms and illustrate how to reduce this confusion. TRIAL REGISTRATION: Current controlled trials ISRCTN50550004 (9 May 2007).
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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 | 0.528 | 0.700 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.027 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".