Identification of Knowledge Gaps Regarding Healthcare Workers' Exposure to Antineoplastic Drugs: Review of Literature, North America versus Europe
Bibliographic record
Abstract
We have been examining the issue of healthcare workers' exposure to antineoplastic drugs for nearly a decade and have observed that there appears to be more publications on the subject matter originating from Europe than from North America. The concern is that findings from Europe may not be generalizable to North America because of differences in handling practices, regulatory requirements, and training. Our objective was to perform a literature review to confirm our observation and, in turn, identify gaps in knowledge that warrants addressing in North America. Using select keywords, we searched for publications in PubMed and Web of Science. All papers were initially classified according to the originating continent and then categorized into one or more subject categories (analytical methods, biological monitoring, occupational exposure, surface contamination, and probability of risk/exposure). Our review identified 16 papers originating from North America and 55 papers from Europe with surface contamination being the subject matter most often studied overall. Based on our results, we are of the opinion that North American researchers need to further conduct dermal and/or urinary drug contamination studies as well as assess the exposure risk faced by healthcare workers who handle antineoplastic drugs. Trends in exposure levels should also be explored.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".