Non-allergic severe asthma: is it really always non-allergic? The IDENTIFY project
Bibliographic record
Abstract
BACKGROUND: This differential diagnosis of allergic vs non-allergic asthma is typically made on the basis of sensitization to allergens, such that absence of sensitization could result in a patient being managed as having non-allergic asthma. In Germany, the number of specific allergen tests is limited and non-standardized (across clinicians and laboratories), with the potential for false negative diagnoses. IDENTIFY aimed to gain data on sensitizations toward aeroallergens in patients with severe asthma who had tested negative to perennial aeroallergens in previous tests. METHODS: This was a single visit, non-randomized, non-interventional study conducted in 87 centers across Germany. The only inclusion criteria were that patients had to be adults (at least 18 years of age) with a diagnosis of severe asthma (receiving at least Global Initiative for Asthma Step IV therapy), and who had previously tested negative to perennial aeroallergens. Patients were then tested for sensitization to a panel of 35 perennial aeroallergens, with positive sensitization indicated by CAP ≥ 0.35 kU/L. RESULTS: Of 588 patients recruited, 454 had complete and valid data, and had previously tested negative to perennial aeroallergens. Overall, 43.6% of the analyzed patients tested positive for at least one of the included aeroallergens, with 18.7% testing positive for three or more, and 4.2% positive for more than ten. The five most common sensitizations were to Staphylococcus aureus enterotoxin B, Aspergillus fumigatus, Candida albicans, Dermatophagoides farinae, and Rhizopus nigricans, each of which tested positive in at least 9.7% of the population. CONCLUSIONS: In this group of patients being managed as having non-allergic asthma (and who had all previously tested negative to perennial aeroallergens), a high proportion tested positive to a broad panel of aeroallergens. A diagnosis of allergic asthma therefore cannot be excluded purely on the basis of standard aeroallergen panels.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".