Bibliographic record
Abstract
PURPOSE OF REVIEW: Occupational asthma (OA) is a complex condition that can be difficult to diagnose. The purpose of this review is to describe some recent findings regarding the epidemiology of OA, the occupational sensitizing agents, the prognosis of OA, and its primary prevention. RECENT FINDINGS: The risk of developing OA varies according to the geographic localization of the worker, the type of industry and the type of sensitizing agents. New findings have been reported for several known sensitizing agents, such as isocyanates, seafood & cleaning agents, and their related industries, such as hairdressing salons and schools. Moreover, a few new sensitizing agents, such as cannabis, have been identified in the past few years. The prognosis of OA seems worse than that of nonwork-related asthma. It is mainly determined by the duration and the level of exposure. Primary prevention is crucial to reduce the number of new cases of OA. Complete avoidance of exposure to the causal agent remains the optimal treatment of sensitizer-induced OA. SUMMARY: Improving our knowledge regarding OA and its causative agents is key to enable an early recognition of this condition and improve its prognosis. Further research is still needed to improve primary 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".