Mobile technology offers novel insights into the control and treatment of allergic rhinitis: The MASK study
Bibliographic record
Abstract
Background Mobile health can be used to generate innovative insights into optimizing treatment to improve allergic rhinitis (AR) control. Objectives A cross-sectional real-world observational study was undertaken in 22 countries to complement a pilot study and provide novel information on medication use, disease control, and work productivity in the everyday life of patients with AR. Methods A mobile phone app ( Allergy Diary , which is freely available on Google Play and Apple stores) was used to collect the data of daily visual analogue scale (VAS) scores for (1) overall allergic symptoms; (2) nasal, ocular, and asthma symptoms; (3) work; and (4) medication use by using a treatment scroll list including all allergy medications (prescribed and over-the-counter) customized for 22 countries. The 4 most common intranasal medications containing intranasal corticosteroids and 8 oral H 1 -antihistamines were studied. Results Nine thousand one hundred twenty-two users filled in 112,054 days of VASs in 2016 and 2017. Assessment of days was informative. Control of days with rhinitis differed between no (best control), single (good control for intranasal corticosteroid–treated days), or multiple (worst control) treatments. Users with the worst control increased the range of treatments being used. The same trend was found for asthma, eye symptoms, and work productivity. Differences between oral H 1 -antihistamines were found. Conclusions This study confirms the usefulness of the Allergy Diary in accessing and assessing behavior in patients with AR. This observational study using a very simple assessment tool (VAS) on a mobile phone had the potential to answer questions previously thought infeasible.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".