Multicenter study of environmental contamination with cyclophosphamide, ifosfamide, and methotrexate in 48 Canadian hospitals
Bibliographic record
Abstract
Context Oncology workers are occupationally exposed to antineoplastic drugs. This exposure can induce adverse health effects. In order to reduce their exposure, contamination on surfaces should be kept as low as possible. Objectives To monitor environmental contamination with cyclophosphamide, ifosfamide, and methotrexate in oncology pharmacy and patient care areas in Canadian hospitals. To describe the impact of some factors that may limit contamination. Methods This is a descriptive study. Twelve standardized sites were sampled in each participating center (six in the pharmacy and six in patient care areas). Samples were analyzed for the presence of cyclophosphamide, ifosfamide, and methotrexate by ultra-performance liquid chromatography tandem mass spectrometry technology. Descriptive statistical analyses were done and results were compared with a Kolmogorov–Smirnov test for independent samples. Results In 2015, 48 hospitals participated in this study (48/202, 24%). Overall, 34% (181/525) of the samples were positive for cyclophosphamide, 8% (41/525) for ifosfamide, and 6% (31/525) for methotrexate. The 75th percentile value of cyclophosphamide surface concentration was 6.9 pg/cm 2 . For ifosfamide and methotrexate, they were lower than the limit of detection. Centers who prepared more antineoplastic drugs per year and centers who used more cyclophosphamide per year showed significantly higher surface contamination ( p < 0.0001). Over the years, we observed a reduction in surface contamination. Conclusion In comparison with other multicenter studies that were conducted in Canada, the concentration of antineoplastic drugs measured on surfaces is decreasing. Regular environmental monitoring is a good practice in order to maintain contamination as low as reasonably achievable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".