The use of sputum eosinophils in the evaluation of occupational asthma
Bibliographic record
Abstract
PURPOSE OF REVIEW: The diagnosis of occupational asthma needs to be made objectively using as many criteria as possible. These include laboratory exposure tests with occupational agent(s), which are only available in specialized centres. Another approach is to monitor peak expiratory flow or methacholine airway responsiveness during periods at work and away from work. However, these measurements are open to misinterpretation when they are not performed optimally. Airway inflammation is one of the main characteristics of occupational asthma, but is not often assessed during its investigation. The purpose of this work was to review recent studies that have investigated and characterized the changes in sputum cell counts occurring in patients with occupational asthma, in order to evaluate the role of the analysis of sputum cell counts. RECENT FINDINGS: There is evidence that monitoring sputum eosinophils can help in the management of asthma. In the majority of cases of occupational asthma, the percentage of sputum eosinophils increases after exposure to occupational agents in the laboratory compared with baseline, but an increase in sputum neutrophils has also been observed. The changes in airway inflammation occurring at the workplace have been less investigated, but indicate that there are significant changes in airway inflammation and especially sputum eosinophils when workers are exposed to a sensitizer at their workplace compared with periods away from the workplace. SUMMARY: Induced sputum has successfully been used to manage patients with mild to moderate asthma. Its use is promising in occupational asthma, and its role in the investigation of occupational asthma needs to be better defined.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.002 | 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".