Phthalate Esters on Hands of Office Workers: Estimating the Influence of Touching Surfaces
Bibliographic record
Abstract
Phthalate esters (PAEs) are known to be transferred to hands by contact with surfaces, however, little is known about the associations between masses on hand wipes and the frequency or duration of touching surfaces, especially surfaces in office environments. Relationships between PAEs on hands and multiple surfaces in offices were investigated. Wipes of hands, computers, and mobile phones as well as dust on furniture were collected from 55 offices in China. Positive associations were found between masses of di-2-ethylhexyl phthalate (DEHP), dibutyl phthalate (DnBP), benzyl butyl phthalate (BBP), and di- n -octyl phthalate (DnOP) on wipes of hands and wipes of keyboards of computers. When workers used keyboards with polymer covers (dust covers), masses of these lipophilic PAEs on hands were significantly correlated with masses on keyboards rather than dust on furniture. For workers who used keyboards without polymer covers, masses on hands were related to masses in dust on furniture. Use of polymer covers containing PAEs and less washing of hands could increase the extent of exposure via hand to body of office workers, which could further result in as much as 10-fold greater hazard. Thus, more hand washing and less use of polymer products containing PAEs were recommended for office workers to reduce exposure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".