Wipe Sampling Method and Evaluation of Environmental Variables for Assessing Surface Contamination of 10 Antineoplastic Drugs by Liquid Chromatography/Tandem Mass Spectrometry
Bibliographic record
Abstract
This paper describes a novel wipe sampling and high-performance liquid chromatography/tandem mass spectrometry (HPLC-MS/MS) method capable of simultaneously detecting 10 antineoplastic drugs (5-fluorouracil, oxaliplatin, methotrexate, vindesine, ifosfamide, cyclophosphamide, vincristine, vinblastine, docetaxel, and paclitaxel). The good overall recoveries and sensitivity values of this method along with the comparatively short run time (8 min) allows for its use in routine monitoring in health care facilities. The long-term behavior of the studied drugs on contaminated surfaces and the effect of surface roughness on drug recoveries were studied to gain insights about how these environmental variables influence the detection, cleaning, and occupational exposure of these drugs. Surfaces with higher roughness parameter (Ra) values (rougher) had the lowest recoveries while those with lower Ra (smoother) presented the highest recoveries. Long-term assessments evidence distinctive drug behaviors with oxaliplatin, vindesine, vincristine, and vinblastine being the less persistent drugs (~20% was recovered after 24 h) and docetaxel and paclitaxel the most persistent drugs with recoveries of 40% and 80% after 1 month. This information indicates the importance of collecting ancillary information about drug usage (throughput, timing, cleaning procedures, etc.) to interpret the results in the context of potential exposure. Finally, the method was successfully applied to evaluate trace surface contamination down to the single picogram per square centimeter in multiple work areas within three local health care centers on Vancouver Island, Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".