Surface Contamination by Antineoplastic Drugs in Two Oncology Inpatient Units
Bibliographic record
Abstract
Abstract Background Hazardous drugs pose risks to health care workers. To reduce the risk of occupational exposure for all workers, several protective and monitoring measures have been recommended and implemented over the past two decades. This study was undertaken to describe traces contamination with ten antineoplastic drugs in the oncology care unit of two university hospitals. Methods In this descriptive interrupted time series study, data was collected in two hospitals (a pediatric hospital and an adult hospital) in two consecutive years (12 December 2017 and 27 March 2018, defined as Period 1; 17 April 2019 and 12 June 2019, defined as Period 2). In both Period 1 and Period 2, 36 sites were sampled in each inpatient care unit to explore the contamination of surfaces with hazardous drugs. Results A total of 144 samples from the oncology care unit of the two hospitals were obtained for measurement. Overall, 40 % (58/144) of the sampling sites were positive for at least one hazardous drug. In the pediatric centre, 50 % (18/36) and 36 % (13/36) of the sites sampled in Period 1 and Period 2, respectively, were positive for at least one hazardous drug, whereas in the adult hospital, the percentage of sites that were positive for at least one hazardous drug was 19 % (7/36) in Period 1 and 56 % (20/36) in Period 2. Conclusion The surfaces of inpatient care units sampled in this study were contaminated with antineoplastic drugs, and contamination was present throughout the care units (including structures, furniture, medical equipment, and office equipment). Hospitals’ environmental surveillance programs should encompass inpatient care units.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".