Novel clinical scores for occupational asthma due to exposure to high-molecular-weight agents
Bibliographic record
Abstract
OBJECTIVE: Specific inhalation challenge (SIC) as the reference diagnostic test for occupational asthma (OA) is not widely available worldwide. We aimed to develop non-SIC-based models for OA. METHODS: Of 427 workers who were exposed to high-molecular-weight agents and referred to OA clinic at Montréal Sacré-Cœur Hospital between 1983 and 2016, we analysed 160 workers who completed non-specific bronchial hyper-responsiveness (NSBHR) tests and still worked 1 month before SIC. OA was defined as positive SIC. Logistic regression models were developed. The accuracy of the models was quantified using calibration and discrimination measures. Their internal validity was evaluated with bootstrapping procedures. The final models were translated into clinical scores and stratified into probability groups. RESULTS: The final model, which included age ≤40 years, rhinoconjunctivitis, inhaled corticosteroid use, agent type, NSBHR, and work-specific sensitisation had a reasonable internal validity. The area under the receiver operating characteristics curve (AUC) was 0.91 (95% CI 0.86 to 0.95), statistically significantly higher than the combination of positive NSBHR and work-specific sensitisation (AUC=0.84). The top 70% of the clinical scores (ie, the high probability group) showed a significantly higher sensitivity (96.4%vs86.9%) and negative predictive value (93.6%vs84.1%) than the combination of positive NSBHR and work-specific sensitisation (p value <0.001). CONCLUSIONS: We developed novel scores for OA induced by high-molecular-weight agents with excellent discrimination. It could be helpful for secondary-care physicians who have access to pulmonary function test and allergy testing in identifying subjects at a high risk of having OA and in deciding on appropriate referral to a tertiary centre.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".