Are Plasma IL-10 Levels a Useful Marker of Human Clinical Tolerance in Peanut Allergy?
Bibliographic record
Abstract
BACKGROUND: Food allergies are a major component of the burden of allergic disease. Accurate risk assessment for prediction of future clinical reactivity or clinical tolerance is limited by currently available techniques. Recent studies suggest that constitutively elevated global serum levels of IL-10, a cytokine that down-regulates both Th1 and Th2 cytokine production, may be useful in identifying human clinical tolerance to foods. OBJECTIVE: Determine the usefulness of constitutive IL-10 levels as a marker of clinical tolerance to peanut in children and adults. METHODOLOGY/PRINCIPAL FINDINGS: 107 subjects who were clinically tolerant to peanut and 94 subjects who were clinically allergic to peanut participated. Plasma was analyzed via ELISA to quantify the frequency of individuals with constitutive IL-10 levels and the intensity of those responses. The data were then stratified by age, gender and clinical status to assess the utility of this putative biomarker in specific at-risk groups. All 201 subjects had readily quantified plasma IL-10. Levels were no higher in subjects who were clinically tolerant to peanut than those in individuals clinically allergic to peanut. Stratification by age, gender or both did not improve the capacity of IL-10 levels to identify clinical tolerance to peanut. CONCLUSIONS/SIGNIFICANCE: Plasma IL-10 levels are neither a useful biomarker of clinical tolerance to peanut nor a potential tool for identification of clinical tolerance to peanut in humans.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".