Induced sputum and exhaled nitric oxide as noninvasive markers of airway inflammation from work exposures
Bibliographic record
Abstract
PURPOSE OF REVIEW: Noninvasive measures of airway inflammation are increasingly used in the investigation and management of asthma. Their role in the investigation of occupational lung diseases, however, is not as clearly established. The present article reviews the use of noninvasive methods - induced sputum and exhaled nitric oxide - in the assessment of airway inflammation during the investigation of occupational asthma and eosinophilic bronchitis, and reviews studies investigating the effect of exposure to various occupational agents on airway inflammation in healthy individuals. RECENT FINDINGS: A number of studies have confirmed the association between exposure to occupational agents and the presence of eosinophilic airway inflammation after that exposure in individuals with occupational asthma. Individuals with positive specific inhalation challenges to occupational agents seem to show a greater increase in exhaled nitric oxide than those with negative specific inhalation challenges. Exposure to various agents associated with an increase in exhaled nitric oxide mainly induced a neutrophilic inflammation. SUMMARY: Increasing evidence supports the use of induced sputum as an additional tool in the investigation of occupational asthma. The role of exhaled nitric oxide in the investigation of occupational asthma needs to be clarified due to conflicting evidence reported in the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".