Evaluation of Current Hazardous Drug Exposure Control in Community Pharmacy
Bibliographic record
Abstract
Purpose : To evaluate effectiveness of current hazardous drug exposure control practices in community pharmacies through identification of commonly contaminated surfaces. We also assessed the decontamination effectiveness of 5 different cleaning agents. Methods : This study was prospective and nonrandomized and conducted in 2 phases. In phase 1, 15 common areas used in the dispensing process were tested at each of 4 pharmacies in Toronto Ontario, Canada. Testing was conducted using the BD ® HD Check System, a rapid, point-of-care, hazardous drug detection system that is able to identify contamination with methotrexate (MTX) and cyclophosphamide (CYP) and doxorubicin. In phase 2, 5 different cleaning agents (70% isopropyl alcohol, Lysol ® spray, Ecolab ® retail multiquat sanitizer, Ecolab retail multisurface and glass cleaner with peroxide, and Ecolab QSR heavy-duty degreaser) were tested for their ability to eliminate contamination. Results : All 4 pharmacies tested positive for contamination with MTX (25.8% of surfaces). Contamination with CYP was less frequent, with only 3 sites and 18.2% of surfaces testing positive. Of the 5 cleaning agents tested, only Ecolab QSR heavy-duty degreaser was able to eliminate contamination with MTX. None of the agents were successful against CYP. Conclusions: The results illustrate an unacceptable prevalence of hazardous drug contamination in community pharmacy settings. The BD HD Check System can serve to rapidly detect common high-risk areas for surface contamination. Decontamination protocols against MTX may include Ecolab QSR heavy-duty degreaser. Novel agents must be identified to remove contamination caused by CYP.
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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 | 0.006 | 0.019 |
| 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 | 0.001 |
| 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".