Work Area Measurements as Predictors of Personal Exposure to Endotoxin and Cotton Dust in the Cotton Textile Industry
Bibliographic record
Abstract
OBJECTIVES: To determine if work area measurements of endotoxin and/or cotton dust obtained from the vertical elutriator (VE) can be used to predict levels of personal endotoxin exposure as measured by the Institute of Occupational Medicine (IOM) inhalable dust sampler in the cotton textile industry. METHODS: Fifty-six work area cotton dust samples were collected from 14 areas and 82 personal cotton dust samples were collected from 41 workers in three textile mills (Mills A, B and C) in Shanghai, China. Cotton dust concentrations were determined gravimetrically from sample filters, of which endotoxin concentrations were determined using a kinetic chromogenic modification of the limulus amoebocyte lysate assay. Linear regression models were used to determine the association between log IOM personal endotoxin concentration and log VE area endotoxin concentration. RESULTS: Median cotton dust and endotoxin concentrations measured from VE area samples in the three mills were 0.36 mg m(-3) and 1280.76 endotoxin units per cubic meter (EU m(-3)), respectively, compared to 1.74 mg m(-3) and 2226.83 EU m(-3) from IOM personal samples. Excluding samples from weaving processes, we observed linear associations between VE area measures of endotoxin and IOM personal endotoxin concentrations; VE area concentration of endotoxin explained 83 and 89% of the total variation in IOM personal endotoxin concentration for Mills A and B, respectively (Mill A: R2 = 0.83, P < 0.0001; Mill B: R2 = 0.89, P < 0.0001). Although area measures of cotton dust was also a significant predictor of person endotoxin, the model explained less of the variance in personal endotoxin measurements. CONCLUSIONS: Specific to the conditions of the textile mills investigated in this study, work area measurements of endotoxin, but not cotton dust, may be reasonable proxies for personal levels, at least for rank-ordering exposures.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".