Work productivity in rhinitis using cell phones: The <scp>MASK</scp> pilot study
Bibliographic record
Abstract
Allergic rhinitis often impairs social life and performance. The aim of this cross-sectional study was to use cell phone data to assess the impact on work productivity of uncontrolled rhinitis assessed by visual analogue scale (VAS). A mobile phone app (Allergy Diary, Google Play Store and Apple App Store) collects data from daily visual analogue scales (VAS) for overall allergic symptoms (VAS-global measured), nasal (VAS-nasal), ocular (VAS-ocular) and asthma symptoms (VAS-asthma) as well as work (VAS-work). A combined nasal-ocular score is calculated. The Allergy Diary is available in 21 countries. The app includes the Work Productivity and Activity Impairment Allergic Specific Questionnaire (WPAI:AS) in six EU countries. All consecutive users who completed the VAS-work from 1 June to 31 October 2016 were included in the study. A total of 1136 users filled in 5818 days of VAS-work. Symptoms of allergic rhinitis were controlled (VAS-global <20) in approximately 60% of the days. In users with uncontrolled rhinitis, approximately 90% had some work impairment and over 50% had severe work impairment (VAS-work >50). There was a significant correlation between VAS-global calculated and VAS-work (Rho=0.83, P<0.00001, Spearman's rank test). In 144 users, there was a significant correlation between VAS-work and WPAI:AS (Rho=0.53, P<0.0001). This pilot study provides not only proof-of-concept data on the work impairment collected with the app but also data on the app itself, especially the distribution of responses for the VAS. This supports the interpretation that persons with rhinitis report both the presence and the absence of symptoms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".