GRADE guidelines 26: informative statements to communicate the findings of systematic reviews of interventions
Bibliographic record
Abstract
OBJECTIVES: Clear communication of systematic review findings will help readers and decision makers. We built on previous work to develop an approach that improves the clarity of statements to convey findings and that draws on Grading of Recommendations Assessment, Development and Evaluation (GRADE). STUDY DESIGN AND SETTING: We conducted workshops including 80 attendants and a survey of 110 producers and users of systematic reviews. We calculated acceptability of statements and revised the wording of those that were unacceptable to ≥40% of participants. RESULTS: Most participants agreed statements should be based on size of effect and certainty of evidence. Statements for low, moderate and high certainty evidence were acceptable to >60%. Key guidance, for example, includes statements for high, moderate and low certainty for a large effect on intervention x as: x results in a large reduction…; x likely results in a large reduction…; x may result in a large reduction…, respectively. CONCLUSIONS: Producers and users of systematic reviews found statements to communicate findings combining size and certainty of an effect acceptable. This article provides GRADE guidance and a wording template to formulate statements in systematic reviews and other decision tools.
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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 | 0.275 | 0.645 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.030 | 0.017 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".