Work-related asthma from cleaning agents versus other agents
Bibliographic record
Abstract
BACKGROUND: Cleaning agents have been commonly implicated as causative or triggering factors in work-related asthma (WRA), mainly from epidemiologic studies. Relatively few clinical series have been reported. AIMS: We aimed to compare socio-demographic and clinical features among tertiary clinic patients with WRA exposed to cleaning and non-cleaning products. METHODS: Analyses were conducted on a patient database containing 208 patients with probable WRA referred to the asthma and airway centre at a tertiary centre hospital in Canada from 2000 to 2014. Chi-squared and independent samples t-tests were used to analyse categorical and continuous data, respectively. RESULTS: Twenty-two (11%) WRA cases were attributed to a variety of cleaning product exposures, 12 were diagnosed as occupational asthma (OA) and 10 as work-exacerbated asthma (WEA) (10% of all OA and 11% of all WEA). There were multiple exposures and the responsible agent(s) could seldom be clearly identified. Most frequent categories of exposure were surfactants, alcohols, disinfectants and acids. Compared to WRA with other exposures, those with cleaning agent exposures had a significantly larger proportion of females (82 versus 35%, P < 0.001), included a higher percentage of workers in healthcare (41 versus 4%, P < 0.001), and submitted more workers' compensation claims (86 versus 64%, P = 0.05). Other characteristics were comparable. CONCLUSIONS: In a tertiary referral clinic, patients with WRA from cleaning agent exposure had clinical characteristics that were similar to those with WRA from other causes. Most frequent exposures were surfactants, alcohols, disinfectants and acids.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".