Using systematic reviews in guideline development: The GRADE approach
Bibliographic record
Abstract
Systematic reviews are essential to produce trustworthy guidelines. To assess the certainty of a body of evidence included in a systematic review the Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group has developed an approach that is currently used by over 100 organisations, including the World Health Organization and the Cochrane Collaboration. GRADE provides operational definitions and instructions to rate the certainty of the evidence for each outcome in a review as high, moderate, low, or very low for the effects of interventions, prognostic estimates, values and preferences, test accuracy and resource utilization. The assessment includes assessing risk of bias, imprecision, inconsistency, indirectness, and publication bias, the magnitude of effects, dose-response relations and the impact of residual confounding and bias. Summary statistical information and assessments of certainty are presented in GRADE evidence summary tables, which can be produced using GRADE's official GRADEpro software tool (www.gradepro.org/). The evidence summary tables feed into the GRADE Evidence to Decision frameworks which guideline panels can use to produce recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.384 | 0.695 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.055 | 0.043 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.013 | 0.010 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".