Specific IgE to Total IgE Ratio Does Not Improve Peanut Diagnostic Accuracy in Adults
Bibliographic record
Abstract
BACKGROUND: Peanut specific IgE (sIgE) can lead to false-positive results. OBJECTIVE: We aimed to assess whether peanut sIgE to total IgE (tIgE) ratio improves accuracy in predicting clinical reactivity to peanut compared to peanut sIgE alone, which has not been explored in the adult population so far. METHOD: A retrospective chart review was performed for adults who underwent peanut oral food challenge (OFC) and/or oral immunotherapy (OIT) at the Centre Hospitalier de l'Université de Montréal's allergy clinic between January 2017 and July 2021. Patients with positive peanut OFC and/or undergoing OIT were considered peanut-allergic. Patients with negative OFC were considered peanut-tolerant. Peanut sIgE to tIgE ratios were calculated and performance characteristics of the sIgE to tIgE ratio were compared to sIgE alone by using receiver operator characteristics curves. RESULTS: Forty-two patients were included (52% male) with a median age of 26 years (range 14-54). Forty-five percent had atopic dermatitis. Median sIgE levels were 2.64 kUA/L (range 0.1-100), median tIgE levels were 154 kUA/L (range 19-3,400), and median sIgE to tIgE ratio was 0.66% (range 0.04-38.3). Twenty-four patients (57%) were classified as peanut-allergic and 18 (43%) as peanut tolerant. The area under the curve for peanut sIgE was 0.921 compared to 0.926 for peanut sIgE/tIgE (p not statistically significant). CONCLUSIONS: We found that there was no significant benefit in using peanut sIgE to tIgE ratio over sIgE alone to predict peanut reactivity in an adult population. Larger prospective studies are needed to further confirm these findings.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".