Monitoring of peanut-allergic patients with peanut-specific IgE
Bibliographic record
Abstract
Peanut allergy affects approximately 1% of the population. Double-blind placebo-controlled food challenges are gold standard for diagnosis. Serum peanut-specific IgE (PN-IgE) is used in clinical practice as an additional diagnostic and monitoring tool. The purpose of this study was to characterize the clinical features of a peanut-allergic patient's cohort and determine the optimum frequency of measuring PN-IgE to predict the outcome of future peanut challenges. Retrospective chart review was performed of peanut-allergic patients followed up and serially tested for PN-IgE with a qualitative antibody fluorescent-enzyme immunoassay performed at the Immunology Laboratory, London Health Sciences Center, from 1997 to 2004. One hundred eighteen patients (median age at first reaction to peanut, 1.5 years; median baseline PN-IgE, 18.75) were reviewed. Younger age at first reaction and first PN-IgE measurement predicted slower decline of PN-IgE values (p < 0.001 and p = 0.044). At 2 and 5 years post-initial measurement, 12.9 and 66%, respectively, of all patients had a significant decrease of PN-IgE values. Twenty percent of the patients experienced elevation of PN-IgE levels during follow-up. For most patients with significant history of reaction to peanuts and positive skin-prick test, it is probably adequate to measure serum PN-IgE levels every 3-5 years to screen for development of tolerance and predict the outcome of future peanut challenges. More frequent measurements might be considered in older patients with lower initial PN-IgE levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".