How DRACMA changes clinical decision for the individual patient in CMA therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: To describe the impact of the diagnosis and rationale for action against cow's milk allergy (DRACMA) guidelines on the decision process in the therapy of cow's milk allergy (CMA). RECENT FINDINGS: We report here the experience of a 2-year application of DRACMA worldwide. Variations in the socioeconomic profile of CMA sufferers and their context can modify the application of DRACMA recommendations. As an example, we use the country-by-country modifications of the social structure and the modifications of the prices for special formula in Italy. SUMMARY: The DRACMA guidelines were issued to inform formula choice for CMA treatment by integrating patients' underlying values, preferences and remarks into grading of recommendations assessment, development and evaluation (GRADE) recommendations, which serve to facilitate their interpretation. This method allows every pediatrician/allergist to follow the changing variables of formulas (cost, palatability, nutritional value) and tailor their prescription for individual patients accordingly. The art of CMA treatment has always relied on physicians' interpretation and the goal of the DRACMA guidelines is to provide a rationale-based and evidence-based indication for choosing an appropriate formula.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".