Biological Monitoring via Urine Samples to Assess Healthcare Workers’ Exposure to Hazardous Drugs: A Scoping Review
Bibliographic record
Abstract
Although biological monitoring is beneficial as it assesses all possible routes of exposure, urine sampling of healthcare workers exposed to hazardous drugs is currently not routine. Therefore, a scoping review was performed on this subject matter to understand what is known about exposure and identify knowledge gaps. A literature search was performed on three databases: ProQuest, Web of Science, and PubMed. Articles published between 2005 and 2020 and written in English were included. Overall, this review consisted of 39 full-text articles. The studies varied with respect to design, sample sizes, sample collection times, and drugs examined. Many articles found at least one sample had detectable levels of a hazardous drug. Studies reported urinary drug contamination despite controls being employed. Knowledge gaps included a lack of an exposure limit, lack of a standardized sampling method, and lack of correlation between health effects and urinary contamination levels. Due to differences in sample collection and analysis, a comparison between studies was not possible. Nevertheless, it appears that biological monitoring via urine sampling is meaningful to aid in understanding healthcare workers’ exposure to hazardous drugs. This is supported by the fact that most studies reported positive urine samples and that case-control studies had statistically significant findings.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".