Comparing approaches to ordering peanut component–resolved diagnostics to reduce the need for oral food challenges
Bibliographic record
Abstract
Background: Peanut component-resolved diagnostics (peanut CRD) is a potentially valuable tool for distinguishing between anaphylactic peanut allergies and milder phenotypes, such as pollen-food allergy syndrome. However, the optimal strategy for integrating CRD into clinical practice remains unclear. Objective: This study aims to evaluate the rates of oral food challenge (OFC) when CRD is ordered: routinely for all patients, selectively on the basis of clinical characteristics, or guided by other peanut biomarkers. Methods: We compared OFC rates between 2 cohorts. Cohort 1 included patients with peanut allergy who received CRD as part of routine testing, regardless of clinical features. In cohort 2, CRD was ordered selectively, depending on factors such as older age, comorbidities, or pollen sensitization. OFC was offered at the physician's discretion in both cohorts. Later, a proposed 2-step clinical algorithm was retrospectively applied to the pooled data to determine patients eligible for OFC. Results: = 0.85). Conclusion: Offering CRD selectively on the basis of clinical characteristics and being guided by a 2-step algorithm are more efficient strategies that can reduce rates of OFC to enhance patient safety, optimize health care resource utilization, and reduce costs. As peanut-specific IgE level and CRD result correlate well, testing for Ara h 2 is likely redundant at very high and low ranges.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".