Antineoplastic Drug Contamination on the Hands of Employees Working Throughout the Hospital Medication System
Bibliographic record
Abstract
We previously reported that antineoplastic drug contamination is found on various work surfaces situated throughout the hospital medication system (process flow of drug within a facility from initial delivery to waste disposal). The presence of drug residual on surfaces suggests that healthcare workers involved in some capacity with the system may be exposed through dermal contact. The purpose of this paper was to determine the dermal contamination levels of healthcare employees working throughout a hospital and to identify factors that may influence dermal contamination. We selected participants from six hospitals and wiped the front and back of workers' hands. Wipe samples were analyzed for cyclophosphamide (CP), a commonly used antineoplastic drug, using high-performance liquid chromatography-tandem mass spectrometry. Participants were asked about their frequency of handling antineoplastic drugs, known contact with CP on their work shift, gender, job title, and safe drug handling training. In addition, participants were surveyed regarding their glove usage and hand washing practices prior to wipe sample collection. We collected a total of 225 wipe samples. Only 20% (N = 44) were above the limit of detection (LOD) of 0.36ng per wipe. The average concentration was 0.36ng per wipe, the geometric mean < LOD, the geometric standard deviation 1.98, and the range < LOD to 22.8ng per wipe. Hospital employees were classified into eight different job categories and all categories had some dermal contamination levels in excess of the LOD. The job category with the highest proportion of samples greater than the LOD were those workers in the drug administration unit who were not responsible for drug administration (volunteer, oncologist, ward aide, dietician). Of note, the highest recorded concentration was from a worker who had no known contact with CP on their work shift. Our results suggest that a broader range of healthcare workers than previously believed, including those that do not directly handle or administer the drugs (e.g. unit clerks, ward aides, dieticians, and shipper/receivers), are at risk of exposure to antineoplastic drugs. A review of control measures to minimize antineoplastic drug exposure that encompasses a wide array of healthcare workers involved with the hospital medication system is recommended.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".