Update on effects of cleaning agents on allergy and asthma
Bibliographic record
Abstract
Background: Cleaning and disinfecting agents are widely used in modern life, in homes, schools, public places, and workplaces as well as in recreational facilities such as swimming pools. Use has been for sanitizing purposes and to assist in reduction of infection as well as for deodorizing purposes. However, adverse respiratory effects have been associated with use of cleaning products ranging from effects in infancy and early childhood up to adults at home and work. Methods: This review summarizes recent published literature on the effects of cleaning agents used pre-natally, in childhood and adult life, at home, work, and in swimming pools. Results: Several studies have indicated that there is an increased risk of developing asthma among adults with frequent exposure to cleaning products at work and in the home. Potential mechanisms include sensitization and respiratory irritant effects. Exposure to irritant chlorine by-products from swimming pools have also been associated with respiratory effects and increased risk of asthma. Potential effects from maternal exposures to cleaning products on infants, and effects on early childhood atopy are less clear. Conclusions: Exposure to cleaning agents increases relative risks of asthma among workers, and adults using these agents in the home. Risks are also increased with exposure to chlorinated by-products from swimming pools, both in adults and children. Further studies are needed to understand the mechanisms of these associations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".