0021 Sensitisation to rats and mice among laboratory staff and researchers
Bibliographic record
Abstract
Objectives To identify modifiable factors associated with sensitisation to laboratory animals. Method Animal husbandry staff (group 1) and researchers (group 2) were recruited from a University animal facility together with health researchers not working with animals (group 3). Sensitisation was evaluated using skin prick tests to rat and mouse allergens. Current tasks, job history and demographic information were recorded. Results The 3 groups comprised: 57; 57; and 50 subjects. Among group 1, 88% were currently working with rats, and 88% with mice: 51% were sensitised to rat, 28% to mouse. In group 2 the numbers exposed were lower (75% rat, 58% mice) as was the rate of sensitisation (32% rat, 12% mouse). No one in group 3 was exposed or sensitised. Among those currently exposed, sensitisation to rat was associated with shaving of fur and disposal of soiled litter, and with less frequent use of female rats. Sensitisation to mice was higher in those with contact with mouse urine and saliva, but not related to specific tasks. In multiple regression models, sensitisation to rat was only associated with use of female rats (OR 0.25, 95% CI 0.01–0.64). Sensitisation to mouse was greater in women (OR 6.67, 1.20–36.98), those born on a farm (OR 4.65, 1.05–20.55) and with exposure to mouse saliva (OR 4.26, 1.23–14.76). Conclusions Exposures were highly correlated, making it difficult to identify specific modifiable risk factors. However it is of note that, since male rats use urine to mark territory, the greater use of exclusively female rats should serve to reduce sensitisation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".