S14– Using the GRADE approach to develop diagnostic guidelines in allergic disease
Bibliographic record
Abstract
Guideline development Accrediting guideline developers Cow milk allergy (CMA) has an incidence of 1.9% to 4.9% during infancy. The World Allergy Organization initiated the development of DRACMA guidelines to provide therapeutic and diagnostic recommendations for the management of this disease. A multidisciplinary guideline panel including 22 members followed the GRADE approach to formulate recommendations. The panel formulated specific questions about the use of skin prick test (SPT), measurement of milk-specific IgE, and allergen microarrays. Panel members rated the importance of patient consequences as having or not a CMA. A systematic review of studies which evaluated the diagnostic accuracy of these tests compared to an OFC was performed. Evidence summaries have shown patient consequences of using each of the tests with low, average, and high initial probability of CMA. Based on this information DRACMA guideline panel made several recommendations about using SPT and measuring milk-specific IgE in the diagnosis of CMA. We identified 26 studies that assessed the accuracy of SPT and 25 studies that assessed the use of milk-specific IgE. Overall quality of evidence supporting the recommendations was low to very low. DRACMA panel made 13 recommendations to use or not to use SPT and/or milk-specific IgE in distinct clinical circumstances. Each recommendation was supplemented by a statement of values and preferences that the panel assumed making judgments about the balance between the desirable and undesirable consequences of using the tests. The GRADE approach creates a link between surrogate outcomes of diagnostic accuracy and patient important outcomes required for decision making that is based on transparent judgments. Our approach provides a process that allows guideline panels to make their process transparent, a key feature of evidence-based guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".