Matching trial design decisions to the needs of those you hope will use the results: the PRECIS-2 tool
Bibliographic record
Abstract
Randomised trials are hard work. Like much that is hard, this toil is only worth it because of the prospect of a substantial reward. Sadly, the reward to potential users such as patients, healthcare professionals and policy makers is often smaller than it should be because trial design decisions reduced the relevance of the trial to them. PRECIS-2 is a tool designed to help trialists match their design decisions to the information needs of those they hope will use the trial results. It is an update of the 2009 PRECIS tool, which though highly cited had some well-known weaknesses. PRECIS-2 was developed in collaboration with over 80 international trialists, methodologists and others to produce a tool that addressed those weaknesses but also supports improved design insight for trialists. PRECIS-2 retains the wheel format of the original tool. It has nine design domains including Eligibility, Recruitment, Setting and Primary outcome. A new domain - Organisation - is explicitly aimed at making trialists consider the resource requirements their intervention will place on health care systems if it were to be rolled out into routine care, the intention being to think about implementation at the design stage. The highly visual presentation makes inconsistent decision-making immediately obvious; it also highlights differences of opinion between trial team members. We will present the tool, explain how to use it and show examples of how it has been used already. This work is part of the Trial Forge initiative to improve trial efficiency.
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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 | 0.417 | 0.723 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.017 |
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".