Assessing and Treating Work-Related Asthma
Bibliographic record
Abstract
: Work-related asthma is asthma that is caused or exacerbated by exposures at work. It is the most common form of occupational lung disease in developed countries. It has important impacts on the health and well-being of the affected individual, as well as consequences for society because of unemployment issues and workers' compensation claims. With ongoing exposure, occupational asthma can result in persistent airway hyperresponsiveness and, possibly, permanent disability for the individual. Thus, it is important for the clinician to be able to diagnose this disorder as quickly and accurately as possible. The evaluation of a patient with work-related asthma can be extensive. It includes obtaining a consistent history, identifying the cause in the workplace, and confirming the diagnosis with objective tests. After a diagnosis has been made, treatment must sometimes go beyond the medications used for nonoccupational asthma and include interventions to minimize or completely remove the individual from exposure to the causal agent if he or she has sensitizer-induced occupational asthma. In addition, once an individual has been identified with occupational asthma, steps should be taken to prevent the development of this disorder in other workers. The purpose of this article is to review the current literature and provide the clinician with a stepwise approach to the diagnosis and management of a patient with work-related asthma.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".