Bibliographic record
Abstract
PURPOSE OF REVIEW: This review updates existing knowledge on occupational rhinitis based on epidemiological and clinical research studies published from 2003 to 2005. RECENT FINDINGS: The review covers new developments on the pathophysiology, diagnosis, monitoring and management of occupational rhinitis. This article also provides updated information on the prevalence and incidence of both occupational rhinitis and work-related symptoms of rhinitis, as well as on causal agents and personal risk factors associated with this condition. SUMMARY: Occupational rhinitis is frequently under-diagnosed due to a lack of physician awareness. Diagnosis is suspected when symptoms occur in relation to work. Differentiating between immunological sensitization and irritation may be difficult. Given the high prevalence of rhinitis in the general population from all causes, objective tests confirming the occupational origin are essential. Measures of inflammatory parameters via nasal lavage and objective assessment of nasal congestion both offer practical means of monitoring responses. Growing experience with acoustic rhinometry and peak nasal inspiratory flow suggests that these methods will have an increasingly important role in monitoring and diagnosing in the future. Recent findings indicate that work-related rhinitis is to some extent preventable. Surveillance of sensitized workers may allow early detection of occupational rhinitis.
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) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".