Development and validation of combined symptom‐medication scores for allergic rhinitis*
Bibliographic record
Abstract
Abstract Background Validated combined symptom‐medication scores (CSMSs) are needed to investigate the effects of allergic rhinitis treatments. This study aimed to use real‐life data from the MASK‐air ® app to generate and validate hypothesis‐ and data‐driven CSMSs. Methods We used MASK‐air ® data to assess the concurrent validity, test‐retest reliability and responsiveness of one hypothesis‐driven CSMS (modified CSMS: mCSMS), one mixed hypothesis‐ and data‐driven score (mixed score), and several data‐driven CSMSs. The latter were generated with MASK‐air ® data following cluster analysis and regression models or factor analysis. These CSMSs were compared with scales measuring (i) the impact of rhinitis on work productivity (visual analogue scale [VAS] of work of MASK‐air ® , and Work Productivity and Activity Impairment: Allergy Specific [WPAI‐AS]), (ii) quality‐of‐life (EQ‐5D VAS) and (iii) control of allergic diseases (Control of Allergic Rhinitis and Asthma Test [CARAT]). Results We assessed 317,176 days of MASK‐air ® use from 17,780 users aged 16‐90 years, in 25 countries. The mCSMS and the factor analyses‐based CSMSs displayed poorer validity and responsiveness compared to the remaining CSMSs. The latter displayed moderate‐to‐strong correlations with the tested comparators, high test‐retest reliability and moderate‐to‐large responsiveness. Among data‐driven CSMSs, a better performance was observed for cluster analyses‐based CSMSs. High accuracy (capacity of discriminating different levels of rhinitis control) was observed for the latter (AUC‐ROC = 0.904) and for the mixed CSMS (AUC‐ROC = 0.820). Conclusion The mixed CSMS and the cluster‐based CSMSs presented medium‐high validity, reliability and accuracy, rendering them as candidates for primary endpoints in future rhinitis trials.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".