Canadian monitoring program of the surface contamination with 11 antineoplastic drugs in 131 centres
Bibliographic record
Abstract
Introduction Handling hazardous drugs contributes to surface contamination in healthcare centres. Their decontamination has proven difficult. Surface monitoring can estimate workers exposure and raise awareness. This program aimed to describe contamination with 11 antineoplastic drugs measured on surfaces of Canadian healthcare centres and their practices, such as the use of dedicated equipment and the communication of results. Methods Each centre sampled six standardized sites in oncology pharmacies and six in outpatient clinics. Ultra-performance liquid chromatography-tandem mass spectrometry quantified cyclophosphamide, docetaxel, doxorubicine, etoposide, 5-fluorouracil, gemcitabine, irinotecan, methotrexate, paclitaxel and vinorelbine. Platinum-based soluble drugs were analysed by inductively coupled plasma mass spectrometry. Centres completed a questionnaire about their practices. Results 131 Canadian hospitals participated in the program. Forty percent (615/1524) of surfaces were contaminated with at least one drug: cyclophosphamide (396/1,524, 26%), gemcitabine (291/1,524, 19%) and platinum (72/805, 9%) were the most frequent. The 90 th percentile of surface concentration was 0.0086 ng/cm² for cyclophosphamide and 0.0028 ng/cm² for gemcitabine. The most contaminated sites were the front grille inside the biological safety cabinet (97/129, 75% contaminated with at least one drug) and the armrest of the treatment chair (92/124, 74%). Both sites were dedicated to hazardous drugs in the majority of centres (114/119, 96% and 91/93, 98%). Most centres (90/116, 78%) had communicated their monitoring results locally. Conclusions Some surfaces were frequently contaminated with low concentration of antineoplastic drugs. Centres should strive to disseminate monitoring results more widely to multidisciplinary teams. These practices can help minimize contamination and ensure a safer working environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".