Noninvasive methods for assessment of airway inflammation in occupational settings
Bibliographic record
Abstract
To cite this article: Quirce S, Lemière C, de Blay F, del Pozo V, Gerth Van Wijk R, Maestrelli P, Pauli G, Pignatti P, Raulf‐Heimsoth M, Sastre J, Storaas T, Moscato G. Noninvasive methods for assessment of airway inflammation in occupational settings. Allergy 2010; 65: 445–458. Abstract The present document is a consensus statement reached by a panel of experts on noninvasive methods for assessment of airway inflammation in the investigation of occupational respiratory diseases, such as occupational rhinitis, occupational asthma, and nonasthmatic eosinophilic bronchitis. Both the upper and the lower airway inflammation have been reviewed and appraised reinforcing the concept of ‘united airway disease’ in the occupational settings. The most widely used noninvasive methods to assess bronchial inflammation are covered: induced sputum, fractional exhaled nitric oxide (FeNO) concentration, and exhaled breath condensate. Nasal inflammation may be assessed by noninvasive approaches such as nasal cytology and nasal lavage, which provide information on different aspects of inflammatory processes (cellular vs mediators). Key messages and suggestions on the use of noninvasive methods for assessment of airway inflammation in the investigation and diagnosis of occupational airway diseases are issued.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".