A new way forward? Examining the potential of quantitative analysis of IgE datasets
Bibliographic record
Abstract
BACKGROUND: Allergies constitute an important public health problem, and epidemiological data is crucial to developing strategies for its prevention and therapy. Few population-based studies are available for data on allergies and sensitization. However, as these studies are expensive and time-consuming, novel approaches are searched for. OBJECTIVES: A large monocentric IgE dataset was used to analyse quantitative sensitization data in different age and gender groups and compared the results to available epidemiological data. METHODS: A total of 14,370 patients who sought medical care at the Department for Dermatology and Allergology at the Technical University of Munich, Germany was analysed. Total IgE and sensitization measured in specific IgE levels to common food allergens and aeroallergens were compared between females and males, age groups, and the year of testing (2003-2021). RESULTS: 8283 females (57.6%) and 6087 males (42.4%) were tested. The average number of specific IgE tests per patient was 12.3 ± 11.4. Total IgE increased after birth with age and reached a peak between 4-6 years in males and 10-12 years in females. Males had higher specific IgE for all common aeroallergens (house dust mite, birch, mugwort and timothy grass pollen) and food allergens (milk protein, chicken egg white, peanut, wheat flour, cod) except for cat epithelia. Data closely reflected results of population-based studies in the literature. CONCLUSION: This study shows that, despite potential patient and test selection bias, the results of the quantitative IgE-dataset analysis closely reflect results of population-based data. Thus, as large cohorts can be examined with a minute amount of effort, this surrogate method appears promising to supplement epidemiology research.
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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 | 0.161 | 0.342 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".