Bibliographic record
Abstract
PURPOSE OF REVIEW: Developments in the understanding of causes and natural history of occupational asthma may allow improved primary, secondary and tertiary preventive strategies for occupational asthma. This may also lead to improved understanding of preventable contributing factors to the development and severity of nonoccupational asthma. RECENT FINDINGS: Animal studies have demonstrated the opportunity to identify chemical sensitizers relevant to asthma. Studies of genetic markers in occupational asthma pose logistic difficulties, but preliminary studies suggest that glutathione S-transferase genotypes may predispose to development of occupational asthma induced by diisocyanates and these have also been implicated in nonoccupational asthma. Some occupational sensitizers/irritants are also found outside the workplace and may be relevant in nonoccupational asthma, for example cleaning agents, epoxy glues, hairdressing products. Accidental exposures to high concentrations of respiratory irritants have the potential to induce new asthma as well as aggravate underlying asthma in both occupational and nonoccupational settings. SUMMARY: Better understanding of the pathogenesis of occupational asthma is important for affected workers, and also has potential relevance for nonoccupational asthma.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".