Deciphering the clinical spectrum of occupational rhinitis
Bibliographic record
Abstract
The paper published in this issue of Occupational and Environmental Medicine by Slager et al entitled “Rhinitis associated with pesticide exposure among commercial pesticide applicators in the Agricultural Health Study” ( see page 718 ) draws attention to several important issues concerning occupational rhinitis1: first, the presumably high prevalence of occupational rhinitis in high risk occupations; second, the growing interest in studying this respiratory condition; and third, some of the difficulties for researchers conducting epidemiological and clinical studies investigating occupational rhinitis due to the lack of standardised and consensus definition and classification of this disease. Cross-sectional studies have demonstrated the frequent occurrence of nasal symptoms in workers exposed to diverse high and low molecular weight agents.2 However, there is great variability in prevalence rates of occupational rhinitis across studies. Since rhinitis is a complex respiratory disease in which environmental and genetic factors may be involved, the observed variability in disease frequency may reflect different influences, including the use of different criteria to define the disease.3 …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".