Cross-sectional evaluation of surface contamination with 9 antineoplastic drugs in 93 Canadian healthcare centers: 2019 results
Bibliographic record
Abstract
Introduction The primary objective was to describe environmental contamination with National Institute for Occupational Safety and Health Group 1 hazardous drugs in oncology pharmacies and outpatient clinics in Canada in 2019, as part of an annual surveillance project. Methods In each participating center, 12 standardized sites (6 in the oncology pharmacy and 6 in outpatient clinic) were sampled. Each sample was prepared to allow quantification of six antineoplastic drugs (cyclophosphamide, ifosfamide, methotrexate, gemcitabine, 5-fluorouracil, and irinotecan) by ultra-performance liquid chromatography-tandem mass spectrometry. Samples were also tested for three additional antineoplastic drugs (docetaxel, paclitaxel, and vinorelbine) without quantification. The impact of certain characteristics of the sampling sites was evaluated with a Kolmogorov–Smirnov test for independent samples. Results Ninety-three Canadian centers participated in 2019, with a total of 1045 surfaces sampled. Cyclophosphamide was the drug most often found in the surface samples (32.4% of samples with positive result), followed by gemcitabine (20.3%). The front grille inside the biological safety cabinet (81.5% of samples positive for at least one antineoplastic drug) and the armrest of a treatment chair (75.8%) were the most frequently contaminated surfaces. Centers with more oncology inpatient and outpatient beds, those that prepared more antineoplastic drugs each year, and those that used more cyclophosphamide each year had higher concentrations of cyclophosphamide contamination on the surfaces tested ( p < 0.0001). Conclusion Traces of dangerous drugs were found in oncology pharmacies and oncology outpatient clinics in 93 Canadian hospitals in 2019. However, the quantities measured were very small. Every healthcare worker should consider these work areas to be contaminated and should wear appropriate protective equipment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".