A systematic review of quality and consistency of clinical practice guidelines on the primary prevention of food allergy and atopic dermatitis
Bibliographic record
Abstract
Background and aims: With an increasing number of Clinical Practice Guidelines (CPGs) addressing primary prevention of food allergy and atopic dermatitis, it is timely to undertake a comprehensive assessment of the quality and consistency of recommendations and evaluation of their implementability in different geographical settings. Methods: We systematically reviewed CPGs from 8 international databases and extensive website searches. Seven reviewers screened records in any language and then used the AGREE II and AGREE REX instruments to critically appraise CPGs published between January 2011 and April 2022. Results: Our search identified 2138 relevant articles, of which 30 CPGs were eventually included. Eight (27%) CPGs were shortlisted based on our predefined quality criteria of achieving scores >70% in the "Scope and Purpose" and "Rigour of Development" domains of the AGREE II instrument. Among the shortlisted CPGs, scores on the "Applicability" domain were generally low, and only 3 CPGs rated highly in the "Implementability" domain of AGREE-REX, suggesting that the majority of CPGs fared poorly on global applicability. Recommendations on maternal diet and complementary feeding in infants were mostly consistent, but recommendations on use of hydrolysed formula and supplements varied considerably. Conclusion: The overall quality of a CPG for Food Allergy and Atopic Dermatitis prevention did not correlate well with its global applicability. It is imperative that CPG developers consider stakeholders' preferences, local applicability, and adapt existing recommendations to each individual population and healthcare system to ensure successful implementation. There is a need for development of high-quality CPGs for allergy prevention outside of North America and Europe. PROSPERO registration number: CRD42021265689.
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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 | 0.110 | 0.457 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".