Surface contamination with ten antineoplastic drugs in 83 Canadian centers
Bibliographic record
Abstract
PURPOSE: The aim of this study was to monitor environmental contamination by 10 antineoplastic drugs in Canadian oncology pharmacy and patient care areas. The secondary objective was to explore the impact of factors that may explain contamination. METHODS: Twelve standardized sites were sampled in each center (six in the pharmacy and six in patient care areas). Each sample was prepared to allow quantification of seven antineoplastic drugs (cyclophosphamide, ifosfamide, methotrexate, cytarabine, gemcitabine, 5-fluorouracil, irinotecan) by UPLC-MS-MS. Docetaxel, paclitaxel and vinorelbine were also detected, but not quantified due to sensibility limitations. The impact of some factors was evaluated compared with a Kolmogorov-Smirnov test for independent samples. RESULTS: ). The arm rest (81.7% of samples positive for at least one antineoplastic drug), the front grille inside the hood (78.3%) and the floor in front of the hood (61.4%) were more frequently contaminated. Centers who prepared more antineoplastic drugs per year had higher concentration on different surfaces ( p < 0.0001). CONCLUSION: Despite growing awareness and implementation of new safe handling guidelines, healthcare centers' surfaces remain contaminated with traces of many antineoplastic drugs. The use of personal protective equipment remains indisputable. Performing an annual monitoring remains a good indicator to monitor trends over time and to compare with similar centers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".