Methodology for development of the Allergic Rhinitis and its Impact on Asthma Guideline 2008 update
Bibliographic record
Abstract
BACKGROUND: We describe the methodology for the 2008 update of the Allergic Rhinitis and its Impact on Asthma (ARIA) guidelines. The methodology differs from the 2001 edition in several respects. The most prominent change is the application of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to compiling evidence, assessing the quality of evidence and grading of recommendations. METHODS AND RESULTS: Representatives of the GRADE working group joined the ARIA guideline panel to achieve these tasks. While most recommendations result from existing systematic reviews, systematic reviews were not always available and the panel compiled the best available evidence in evidence profiles without conducting actual reviews. The panel conducted two meetings and used the GRADE criteria to assess the quality of evidence (four categories of high, moderate, low and very low) and the strength of recommendation (strong and weak) based on weighing up the desirable and undesirable effects of management strategies, considering values and preferences influencing recommendations, and resource implications. The guideline panel has chosen the words 'we recommend'--for strong recommendations and 'we suggest'--for weak recommendations. Both categories indicate the best course of action for a given patient population, but their implementation, requires different considerations as we describe subsequently in this article. CONCLUSIONS: The 2008 update of the ARIA guidelines has become more evidence-based. Future iterations of the guidelines will further be improved by following the described processes even closer, such as ensuring availability of updated high quality systematic reviews for each question.
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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 | 0.159 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".