Are cell phones an indicator of personal exposure to organophosphate flame retardants and plasticizers?
Bibliographic record
Abstract
BACKGROUND: Exposure to organophosphate ester (OPE) flame retardants and plasticizers is widespread and is of concern due to their toxicity. OBJECTIVES: To investigate relationships between and within OPE concentrations in air, dust, hands, electronic product wipes and urinary metabolites with the goal of identifying product sources and exposure pathways. METHODS: Women in Toronto and Ottawa, Canada, provided a urine sample, two sets of hand wipes, access to their homes for air and dust sampling, and completed a questionnaire. OPE concentrations were obtained for air and floor dust in the bedroom (n = 51) and most used room (n = 26), hand wipes (n = 204), and surface wipes of handheld (n = 74) and non-handheld electronic devices (n = 125). All air, dust and wipe samples were analyzed for 23 OPE compounds; urine samples (n = 44) were analyzed for 8 OPE metabolites. RESULTS: Five-8 OPEs were detected in >80% of samples depending on the sample type. OPE median concentrations in hand wipes taken 3 weeks apart were not significantly different. Palms had higher concentrations than the back of hands; both were significantly correlated. Concentrations of 9 OPEs were significantly higher in surface wipes of handheld than non-handheld electronic devices. Six OPEs in hand wipes were significantly correlated with cell phone wipes, with two to four OPEs significantly correlated with tablet, laptop and television wipes. Multiple regression models using hand wipes, cell phone wipes and dust explained 8-33% of the variation in creatinine-adjusted urinary metabolites; air concentrations did not have explanatory power. OPEs in cell phone wipes explained the greatest variation in urinary metabolites. CONCLUSIONS: Handheld electronic devices, notably cell phones, may either be sources or indicators of OPE exposure through hand-to-mouth and/or dermal uptake.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".