A Novel Questionnaire and Algorithm for Work‐Related Asthma Screening and Surveillance: An <scp>EAACI</scp> Task Force Report
Bibliographic record
Abstract
INTRODUCTION: Work-related exposures contribute to one in six new-onset adult asthma cases and exacerbation of one in five existing cases, which together are termed 'work-related asthma' (WRA). A valid and standardized WRA questionnaire is needed for workplace surveillance and epidemiological studies. This project aimed to review evidence on WRA questionnaires and algorithms to propose a standardized instrument. METHODS: A scoping review was conducted using PubMed, Embase, and Cochrane databases up to March 2021. Search terms focused on asthma, occupational diseases, questionnaires, surveys, and algorithms. High-quality studies were identified and data extracted on instrument construction, validation, and performance. Common questions were used to develop a questionnaire and algorithm for detecting suspected WRA. RESULTS: Six studies were included. The final WRA questionnaire consists of eight questions on general asthma symptoms, diagnosis, and medication; four on WRA symptoms; and two on work-related ocular-nasal symptoms. The algorithm calculates a WRA total score (WRATS) based on the general asthma and work-related symptoms. A score of ≥ 1 triggers a referral for further evaluation. CONCLUSION: This is the first WRA questionnaire based on validated questionnaires. Evaluation of its performance and validation in diverse geographic and occupational settings are needed for further refinement and translation for broader applications.
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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 | 0.061 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".