What are the questionnaire items most useful in identifying subjects with occupational asthma?
Bibliographic record
Abstract
The present study assessed the usefulness of key items obtained from a clinical "open" questionnaire prospectively administered to 212 subjects, referred to four tertiary-care hospitals for predicting the diagnosis of occupational asthma (OA). Of these subjects, 72 (34%) were diagnosed as OA (53% with OA due to high-molecular-weight agents) according to results of specific inhalation challenges, and 90 (42%) as non-OA. Wheezing at work occurred in 88% of subjects with OA and was the most specific symptom (85%). Nasal and eye symptoms were commonly associated symptoms. Wheezing, nasal and ocular itching at work were positively, and loss of voice negatively associated with the presence of OA in the case of high-, but not low molecular-weight agents. A prediction model based on responses to nasal itching, daily symptoms over the week at work, nasal secretions, absence of loss of voice, wheezing, and sputum, correctly predicted 156 out of 212 (74%) subjects according to the presence or absence of OA by final diagnosis. In conclusion, key items, i.e. wheezing, nasal and ocular itching and loss of voice, are satisfactorily associated with the presence of occupational asthma in subjects exposed to high-molecular-weight agents. Therefore, these should be addressed with high priority by physicians. However, no questionnaire-derived item is helpful in subjects exposed to low-molecular-weight agents.
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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 | 0.003 | 0.016 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".