Natural history of sensitization, symptoms and occupational diseases in apprentices exposed to laboratory animals
Bibliographic record
Abstract
The natural history of the development of sensitization and disease due to high-molecular-weight allergens is not well characterized. This study describes the time-course of the incidence of work-related symptoms, skin reactivity and occupational rhinoconjunctivitis (RC) and asthma (OA); and assesses the predictive value of skin testing and RC symptoms in apprentices exposed to laboratory animals, in a 3-4-yr programme. Four-hundred and seventeen apprentices at five institutions were assessed prospectively with questionnaire, skin-testing with animal-derived allergens, spirometry and airway responsiveness (n=373). Depending on the school, students were seen 8 (n=136), 20 (n=345), 32 (n=355) and 44 (n=98) months after starting the programme. At all visits, the incidence was greater for work-related RC symptoms followed in order by skin reactivity, occupational RC, and, almost equally, OA and work-related respiratory symptoms. The incidence-density figures were comparable for each follow-up period and for most indices up to 32 months after entry into the study and then tended to decrease. The positive predictive values (PPVs) of skin reactivity to work-related allergens for the development of work-related RC and respiratory symptoms were 30% and 9.0%, respectively, while the PPVs of work-related RC for the development of OA was 11.4%. Sensitization, symptoms and diseases occur maximally in the first 2-3 yrs after starting exposure to laboratory animals. Skin reactivity to work-related allergens and rhinoconjuctivitis symptoms have low positive predictive values.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".