Grading quality of evidence and strength of recommendations in clinical practice guidelines Part 3 of 3. The GRADE approach to developing recommendations
Bibliographic record
Abstract
To cite this article: Brożek JL, Akl EA, Compalati E, Kreis J, Terracciano L, Fiocchi A, Ueffing E, Andrews J, Alonso-Coello P, Meerpohl JJ, Lang DM, Jaeschke R, Williams JW Jr, Phillips B, Lethaby A, Bossuyt P, Glasziou P, Helfand M, Watine J, Afilalo M, Welch V, Montedori A, Abraha I, Horvath AR, Bousquet J, Guyatt GH, Schünemann HJ, for the GRADE Working Group. Grading quality of evidence and strength of recommendations in clinical practice guidelines. Part 3 of 3. The GRADE approach to developing recommendations. Allergy 2011; 66: 588–595. This is the third and last article in the series about the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to grading the quality of evidence and the strength of recommendations in clinical practice guidelines and its application in the field of allergy. We describe the factors that influence the strength of recommendations about the use of diagnostic, preventive and therapeutic interventions: the balance of desirable and undesirable consequences, the quality of a body of evidence related to a decision, patients’ values and preferences, and considerations of resource use. We provide examples from two recently developed guidelines in the field of allergy that applied the GRADE approach. The main advantages of this approach are the focus on patient important outcomes, explicit consideration of patients’ values and preferences, the systematic approach to collecting the evidence, the clear separation of the concepts of quality of evidence and strength of recommendations, and transparent reporting of the decision process. The focus on transparency facilitates understanding and implementation and should empower patients, clinicians and other health care professionals to make informed choices.
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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 | 0.289 | 0.625 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.053 | 0.037 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 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".