How pragmatic or explanatory is the randomized, controlled trial? The application and enhancement of the PRECIS tool to the evaluation of a smoking cessation trial
Bibliographic record
Abstract
BACKGROUND: Numerous explanatory randomized trials support the efficacy of chronic disease interventions, including smoking cessation treatments. However, there is often inadequate adoption of these interventions for various reasons, one being the limitation of generalizability of the explanatory studies in real-world settings. Randomized controlled trials can be rated as more explanatory versus pragmatic along 10 dimensions. Pragmatic randomized clinical trials generate more realistic estimates of effectiveness with greater relevance to clinical practice and for health resource allocation decisions. However, there is no clear method to scale each dimension during the trial design phase to ensure that the design matches the intended purpose of the study. METHODS: We designed a pragmatic, randomized, controlled study to maximize external validity by addressing several barriers to smoking cessation therapy in ambulatory care. We analyzed our design and methods using the recently published 'Pragmatic-Explanatory Continuum Indicatory Summary (PRECIS)' tool, a qualitative method to assess trial design across 10 domains. We added a 20-point numerical rating scale and a modified Delphi process to improve consensus in rating these domains. RESULTS: After two rounds of review, there was consensus on all 10 domains of study design. No single domain was scored as either fully pragmatic or fully explanatory; but overall, the study scored high on pragmatism. CONCLUSIONS: This addition to the PRECIS tool may assist other trial designers working with interdisciplinary co-investigators to rate their study design while building consensus.
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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 | 0.838 | 0.924 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".