PRECIS-2: a tool to improve the applicability of randomised controlled trials
Bibliographic record
Abstract
Results Brainstorming sessions identified the PRECIS presentation (a wheel), lack of a scoring system and domain weighting as issues for exploration in the Delphi process. Thirty four completed responses from 90 invitees were received in Round 1 of the Delphi; Round 2 involved 23 individuals (response rate 82%). 45% selected a 1-5 Likert scale, 56.5% wanted to use a table (to justify decisions) and a PRECIS wheel, 26% were in favour of weighting domains. Suggestions for extra domains included: recruitment process for participants and integration of the intervention into the healthcare system. An expert panel in Toronto used the Delphi suggestions to help create alternative versions of PRECIS-2 for user-testing in spring 2013. Conclusions PRECIS can be improved by the addition of a Likert scale and additional domains. We expect to have a validated PRECIS-2 by the beginning of 2014.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.171 | 0.310 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; both teacher heads 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".