Comments, suggestions, and criticisms of the Pragmatic Explanatory Continuum Indicator Summary-2 design tool: a citation analysis
Bibliographic record
Abstract
INTRODUCTION: The pragmatic explanatory continuum indicator summary (PRECIS) tool, initially published in 2009 and revised in 2015, was created to assist trialists to align their design choices with the intended purpose of their randomised controlled trial (RCT): either to guide real-world decisions between alternative interventions (pragmatic) or to test hypotheses about intervention mechanisms by minimising sources of variation (explanatory). There have been many comments, suggestions, and criticisms of PRECIS-2. This summary will be used to facilitate the development of to the next revision, which is PRECIS-3. METHODS: We used Web of Science to identify all publication types citing PRECIS-2, published between May 2015 and July 2023. Citations were eligible if they contained 'substantive' suggestions, comments, or criticism of the PRECIS-2 tool. We defined 'substantive' as comments explicitly referencing at least one PRECIS-2 domain or a concept directly linked to an existing or newly proposed domain. Two reviewers independently extracted comments, suggestions, and criticisms, noting their implications for the update. These were discussed among authors to achieve consensus on the interpretation of each comment and its implications for PRECIS-3. RESULTS: The search yielded 885 publications, and after full-text review, 89 articles met the inclusion criteria. Comments pertained to new domains, changes in existing domains, or were relevant across several or all domains. Proposed new domains included assessment of the comparator arm and a domain to describe blinding. There were concerns about scoring eligibility and recruitment domains for cluster trials. Suggested areas for improvement across domains included the need for more scoring guidance for explanatory design choices. DISCUSSION: Published comments recognise PRECIS-2's success in aiding trialists with pragmatic or explanatory design choices. Enhancing its implementation and widespread use will involve adding new domains, refining domain definitions, and addressing overall tool issues. This citation review offers valuable user feedback, pivotal for shaping the upcoming version of the PRECIS tool, PRECIS-3.
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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 | 0.511 | 0.888 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.035 | 0.031 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".