Novel Diagnostic Models for Occupational Asthma from Low-Molecular-Weight Agents Exposure
Bibliographic record
Abstract
Objective: The specific inhalation challenge (SIC) is the reference standard for diagnosing occupational asthma (OA) but is not widely available globally. We aimed to develop non-SIC-based models to diagnose OA in workers exposed to low-molecular-weight (LMW) agents. Methods: We conducted a diagnostic study on SIC positivity using clinical interview variables and non-SIC tests. Retrospective data (1980–2020) from two tertiary centers in British Columbia and Quebec included individuals exposed to LMW agents. We developed the models using logistic regression and externally validated them in centers with routine SIC (Finland/Poland) and centers where expert(s) confirmed OA diagnosis without routine SIC (Ontario/Turkey). Results: Our clinical interview model, which included male sex, isocyanate exposure, work-related rhinoconjunctivitis, smoking status, inhaled corticosteroid use, and exposure duration <10 years, had an area under the receiver operating characteristics curve (AUC) of 0.65. Adding diagnostic tests improved AUCs: interview plus sputum induction cell count (AUC=0.71), nonspecific bronchial hyperreactivity (NSBHR, AUC=0.72), serial peak expiratory flow (PEF, AUC=0.78), and NSBHR plus serial PEF (AUC=0.80). The final model, combining the clinical interview with serial PEF, had a shrinkage factor of 0.94, indicating good internal validity, and a Brier score of 0.156, reflecting good calibration. In Finland/Poland, the clinical interview alone had an AUC of 0.67, which improved to 0.84 with serial PEF, while in Ontario/Turkey, the AUC increased from 0.59 to 0.67. Conclusion: A novel model composed of clinical interviews and serial PEF can predict positive SIC caused by LMW 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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".