Exaggerated bronchoconstriction due to inhalation challenges with occupational agents
Bibliographic record
Abstract
Inhalation challenges with occupational agents are used to confirm the aetiology of occupational asthma. It has been proposed that using closed-circuit equipment rather than the realistic challenge method would improve the methodology of these tests. Changes in forced expiratory volume in one second (FEV1) were examined in 496 subjects with "positive specific inhalation challenges", i.e. changes in FEVI of > or = 20% after exposure to an occupational agent, including 357 subjects exposed by the realistic method, 108 using the closed-circuit method and 31 by both methods. For immediate reactions, 18 of 95 (19%) showed changes in FEV1 of > or = 30% with the closed-circuit method, whereas a significantly larger proportion, i.e. 77 of 200 (38.5%), showed such changes using the realistic method. As regards nonimmediate reactions, changes in FEV1 of > or = 30% occurred in 16 of 43 (37%) cases with the closed-circuit method as compared to a larger proportion, i.e. 87 of 180 (48%) cases, using the realistic method. This favourable effect was significantly more pronounced in workers with higher levels of bronchial hyperresponsiveness to methacholine. It is concluded that, for agents that can be generated using the closed-circuit method, use of such apparatus results in a smaller proportion of exaggerated bronchoconstriction than does the realistic method, this being particularly true for 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".