Daily allergic multimorbidity in rhinitis using mobile technology: A novel concept of the <scp>MASK</scp> study
Bibliographic record
Abstract
BACKGROUND: Multimorbidity in allergic airway diseases is well known, but no data exist about the daily dynamics of symptoms and their impact on work. To better understand this, we aimed to assess the presence and control of daily allergic multimorbidity (asthma, conjunctivitis, rhinitis) and its impact on work productivity using a mobile technology, the Allergy Diary. METHODS: We undertook a 1-year prospective observational study in which 4 210 users and 32 585 days were monitored in 19 countries. Five visual analogue scales (VAS) assessed the daily burden of the disease (i.e., global evaluation, nose, eyes, asthma and work). Visual analogue scale levels <20/100 were categorized as "Low" burden and VAS levels ≥50/100 as "High" burden. RESULTS: Visual analogue scales global measured levels assessing the global control of the allergic disease were significantly associated with allergic multimorbidity. Eight hypothesis-driven patterns were defined based on "Low" and "High" VAS levels. There were <0.2% days of Rhinitis Low and Asthma High or Conjunctivitis High patterns. There were 5.9% days with a Rhinitis High-Asthma Low pattern. There were 1.7% days with a Rhinitis High-Asthma High-Conjunctivitis Low pattern. A novel Rhinitis High-Asthma High-Conjunctivitis High pattern was identified in 2.9% days and had the greatest impact on uncontrolled VAS global measured and impaired work productivity. Work productivity was significantly correlated with VAS global measured levels. CONCLUSIONS: In a novel approach examining daily symptoms with mobile technology, we found considerable intra-individual variability of allergic multimorbidity including a previously unrecognized extreme pattern of uncontrolled multimorbidity.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".