Occupational Exposure to Antineoplastic Drugs: Identification of Job Categories Potentially Exposed throughout the Hospital Medication System
Bibliographic record
Abstract
Objectives: Studies examining healthcare workers' exposure to antineoplastic drugs have focused on the drug preparation or drug administration areas. However, such an approach has probably underestimated the overall exposure risk as the drugs need to be delivered to the facility, transported internally and then disposed. The objective of this study is to determine whether drug contamination occurs throughout a facility and, simultaneously, to identify those job categories that are potentially exposed. Methods: This was a multi-site study based in Vancouver, British Columbia. Interviews were conducted to determine the departments where the drugs travel. Subsequent site observations were performed to ascertain those surfaces which frequently came into contact with antineoplastic drugs and to determine the job categories which are likely to contact these surfaces. Wipe samples were collected to quantify surface contamination. Results: Surface contamination was found in all six stages of the hospital medication system. Job categories consistently found to be at risk of exposure were nurses, pharmacists, pharmacy technicians, and pharmacy receivers. Up to 11 job categories per site may be at risk of exposure at some point during the hospital medication system. Conclusion: We found drug contamination on select surfaces at every stage of the medication system, which indicates the existence of an exposure potential throughout the facility. Our results suggest that a broader range of workers are potentially exposed than has been previously examined. These results will allow us to develop a more inclusive exposure assessment encompassing all healthcare workers that are at risk throughout the hospital medication system.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".