Surface contamination with nine antineoplastic drugs in 109 canadian centers; 10 years of a monitoring program
Why this work is in the frame
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Bibliographic record
Abstract
Introduction Healthcare workers exposure to antineoplastic drugs can lead to adverse health effects. Guidelines promote the safe handling of antineoplastic drugs, but no safe exposure limit was determined. Regular surface sampling contributes to ensuring workers safety. Methods A cross-sectional monitoring is conducted once a year with voluntary Canadian centers, since 2010. Twelve standardized sampling sites were sampled. Samples were analyzed by high performance mass coupled liquid chromatography. The limits of detection (in ng/cm 2 ) were: 0.001 for cyclophosphamide and gemcitabine; 0.3 for docetaxel and ifosfamide; 0.04 for 5-fluorouracil and paclitaxel; 0.003 for irinotecan; 0.002 for methotrexate; 0.01 for vinorelbine. Results The surfaces from 109 centers were sampled between 01/01/2020–18/06/2020. Twenty-six centers delayed their participation because of the COVID-19 pandemic. 1217 samples were analyzed. Surfaces were frequently contaminated with cyclophosphamide (34% positive, 75th percentile 0.00165 ng/cm 2 ) and gemcitabine (16% and <0.001 ng/cm 2 ). The armrest of patient treatment chairs (84% to at least one drug), the front grille inside the biological safety cabinet (BSC) (73%) and the floor in front of the BSC (55%) were frequently contaminated. Centers that prepared ≥5000 antineoplastic drugs annually had higher concentration of cyclophosphamide on their surfaces (p < 0.0001). Contamination measured on the surfaces was reduced from 2010 to 2020. Conclusions This large-scale study showed reproducible long term follow up of the contamination of standardized sites of Canadian centers and a reduction in surface contamination from 2010 to 2020. Periodic surface sampling help centers meet their continuous improvements goals to reduce exposure as much as possible. The COVID-19 pandemic had a limited impact on the program.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it