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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| 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".