Antineoplastic Drug Contamination of Surfaces Throughout the Hospital Medication System in Canadian Hospitals
Bibliographic record
Abstract
We previously reported that there is a potential for antineoplastic drug contamination throughout the hospital medication system (process flow of drug within a facility from delivery to waste disposal) due to the various surfaces contacted by health care workers. This article describes the contamination of these frequently contacted surfaces as well as identifies factors that may be associated with surface contamination. Surfaces which health care workers frequently contact were wiped and the concentration of cyclophosphamide (CP) was determined using high-performance liquid chromatography-tandem mass spectrometry. Descriptive and inferential statistical analyses were performed. A backward stepwise multiple linear regression was conducted to identify determinants associated with surface contamination. Overall, 229 surfaces were sampled, most on two occasions, for a total of 438 surface wipes. The mean CP concentration was 0.201 ng/cm(2), the geometric mean 0.019 ng/cm(2), and the geometric standard deviation 2.54, with a range of less than detection (LOD) to 26.1 ng/cm(2). (Method LOD was 0.356 ng/wipe; factoring in the surface area of the wiped surface, results in a sample LOD ranging from 0.00 to 0.049 ng/cm(2)). Our study found that frequently contacted surfaces at every stage of the hospital medication system had measureable levels of antineoplastic drug contamination. Two factors were statistically significant with respect to their association with surface contamination: (1) the stage of the hospital medication system, and (2) the number of job categories responsible for drug transport. The drug preparation stage had the highest average contamination. Those hospitals that had two or more drug transport job categories had higher levels of surface contamination. Neither the reported handling of CP prior to wipe sampling nor the cleaning of surfaces appeared to be associated with contamination.
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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 | 0.001 | 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".