Silicone wristbands integrate dermal and inhalation exposures to semi-volatile organic compounds (SVOCs)
Bibliographic record
Abstract
Silicone wristbands are being increasingly used to assess human exposure to semi-volatile organic compounds (SVOCs). However, it is unclear what exposure pathways wristbands integrate. To test the hypothesis that wristbands integrate inhalation and dermal exposures, we measured 38 chemicals from four compound groups (PAHs, PBDEs, nBFRs, and OPEs) in silicone wristbands and brooches, active air samples (Occupational Safety and Health Administration Versatile Sampler or OVS cartridge), and hand wipes from 10 adults during a 72-hour period. Phenanthrene, BDE-47, 2‑ethylhexyl 2,3,4,5‑tetrabromobenzoate (EHTBB), tris[(2R)‑1‑chloro‑2‑propyl] phosphate (TCIPP), and tris(1,3‑dichloro‑2‑propyl) phosphate (TDCIPP) were the predominant compounds in all four matrices. In a linear regression analysis, the compound levels in OVS were positively associated with those in wristbands and brooches for nBFRs and OPEs, but not for PAHs and PBDEs. The compound levels in wristbands were positively associated with those in hand wipes and brooches for all chemicals. The regressions between the levels in wristbands and OVS or brooches combined with the levels in hand wipes showed stronger, supporting the hypothesis that wristbands captured inhalation and dermal exposure pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".