Diagnosis of nasal and eye allergies: the Allergies, Immunotherapy, and RhinoconjunctivitiS (AIRS) patient survey
Bibliographic record
Abstract
Knowledge and avoidance of allergies can lead to better allergy symptom control. The AIRS study assessed approaches to diagnosis of nasal and eye allergies or allergic rhinoconjunctivitis in the US. A national sample of 34,030 households using a dual frame approach of random digit dialing and cell phone sample were targeted. Patients aged =5 years with a health care professional diagnosis of hay fever, allergic rhinitis, rhino-conjunctivitis, nasal or eye allergies and symptoms or medication for condition in past 12 months were surveyed. Data on specific diagnosis and allergy testing were collected. Based on screening land-line sample of 20,835 households, 18% of individuals were diagnosed with one of conditions of interest. Of the 2765 surveyed patients, 86% were diagnosed with nasal allergies, 59%, hay fever, 54% eye allergies, 30%, allergic rhinitis and 13%, rhinoconjunctivitis. Four percent of respondents reported they were given an allergy test by doctor or health professional in past four months, 3% in past year, 7%, 1-2 years ago, and 37%, 3 or more years ago. Of those, 71% reported that they had a skin prick test, 13% had blood test and 12% had both blood and skin prick test. 47% report they never received an allergy test. The AIRS survey demonstrated that 18% of individuals > 5 years were diagnosed with an above allergic condition. Although these respondents had been diagnosed by a health care professional with “allergies”, almost half never had any allergy testing to determine the triggers of their condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".