New insights into occupational asthma
Bibliographic record
Abstract
PURPOSE OF REVIEW: To examine recent publications on the types of agents involved in occupational asthma, the mechanisms by which they induce asthma, and how best to evaluate and treat workers suspected of this respiratory condition. RECENT FINDINGS: High rates of occupational asthma and inhalation accidents were found in workers in crafts and related occupations in the manufacturing industries, and in plant and machine operatives; cleaners and construction workers may also be at risk. Further data support a role for CD4 T cells in low-molecular-weight agent-induced asthma, such as with isocyanates, and neurogenic mechanisms may also be involved. The use of noninvasive measures of airway inflammation in the diagnosis and management of occupational asthma such as sputum eosinophils monitoring is promising, although this is less obvious for exhaled nitric oxide. Finally, the persistence of troublesome asthma even after withdrawal from relevant exposure has been re-emphasized and surveillance programs have been proposed. SUMMARY: Further data have been gathered on the prevalence of occupational asthma in various working populations, its mechanisms of development, the contribution of noninvasive measures of airway inflammation in the diagnosis and management of this condition, and its management and prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".