Evaluation and Application of the Work-Related Asthma Screening Questionnaire—Long Version (WRASQ[L])
Bibliographic record
Abstract
BACKGROUND: The Work-related Asthma (WRA) Screening Questionnaire-Long Version (WRASQ(L)) is a screening questionnaire that could improve the recognition of WRA. OBJECTIVE: To conduct a definitive evaluation of the WRASQ(L) to justify its implementation in clinical settings. METHODS: Employed adults aged 18 to 75 years with asthma confirmed by objective measures and the ability to take time off work were eligible. Participants completed the WRASQ(L) and then monitored their peak expiratory flow at and away from work or completed a specific inhalation challenge test. Data were classified as WRA or non-WRA by 2 asthma specialists, blinded to WRASQ(L) answers. Sensitivity (SN), specificity (SP), positive and negative predictive values (PPV and NPV, respectively), and Youden's index were calculated for cutoffs of a positive screen. RESULTS: Of 106 participants (47.1 ± 7.1 years [mean ± standard deviation]; 60 [57%] female), 14 (17%) were classified as having WRA and were significantly younger in age than non-WRA participants (P = .043). The questionnaire has high SN and NPV (90.9% and 93.1%, respectively) but low PPV and SP (32.1% and 26.0%, respectively). CONCLUSIONS: The WRASQ(L) has high SN and NPV. High SN is of primary interest to ensure that few false-negative screens are missed and those with potential WRA are identified and continue to specialist care. The SN indicates utility of the questionnaire in clinical settings. Further benefits of the tool include its potential to prompt education on the symptom-workplace relationship, workplace exposures, personal protective equipment use, and collect exposure and occupational history.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".