Wipe sampling of antineoplastic drugs from workplace surfaces: A review of analytical methods and recommendations
Bibliographic record
Abstract
Antineoplastic drugs (ADs) are primarily used to manage cancer and are becoming more prevalent as cancer cases increase globally. A primary route of exposure for workers administering these hazardous drugs is via dermal absorption from contact with contaminated surfaces. To assess the risk in a workplace, wipe sampling is used to inspect and assess suspect workplace surfaces (i.e., as a form of worker exposure and environmental monitoring). Despite widespread use of ADs, there are no standards or proficiency testing programs (at this time) for surface wipe sampling protocols specifically for residues that contain ADs. Current literature provides many different analytical methods developed by research groups for detecting one or more ADs in residue found in the workplace. These studies contain significant variability in the techniques and materials used and, therefore, also vary in their outcomes. This review highlights the variability observed in the results obtained from current methods and points to opportunities that might assist in addressing these inconsistencies towards preparing standard methodologies for wipe sampling of ADs. This review also discusses critical factors to consider when optimizing the steps performed for surface wipe sampling. The inter-dependent steps discussed in this review for surface wipe sampling are: (i) adsorption of analyte onto a wipe; (ii) desorption of the analyte from the wipe; and (iii) detection of the sampled analyte. The first two steps require optimization of both chemical and physical factors to create a successful sampling method. The detection step has largely been optimized due in part to the sensitivity of analytical instrumentation, but there remain opportunities to develop more effective methodologies for timely feedback and an increased sensitivity to platinum-based ADs. This review also provides additional recommendations to improve reporting of results from the wipe sampling of ADs and highlights the need for additional research on the occupational and surface exposure limits for ADs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".