Bibliographic record
Abstract
CME Educational Objectives 1. Discuss the pre-test clinical assessment of a patient with a suspected food allergy. 2. Review the currently available diagnostic tests and their performance for frequent food allergens. 3. Interpret allergy tests in light of the pre-test assessment to determine final probability of food allergy and indication for referral for food challenge. Diagnosis of food allergy can be challenging. Given the limited specificity of available allergy tests, these need to be interpreted in light of pre-test probability that is determined by a careful history. Using likelihood ratios calculated from previous publication may allow a more individualized assessment. This approach is likely to be most useful in patients with low to moderate results, below the 95% positive predictive value for that food. This review covers the diagnostic approach of immunoglobulin E-mediated food allergy. We first focus on the pre-test clinical assessment of a patient with a suspected food allergy. We then compare currently available diagnostic tests and discuss their performance for frequent food allergens. Finally, we conclude with the interpretation of allergy tests in light of the pre-test assessment to determine final probability of food allergy and indications for referral to an allergy specialist for food challenge.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.025 |
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".