Surface Contamination in a Teaching Hospital: A 6 Year Perspective
Bibliographic record
Abstract
Abstract Objective: The aim of this paper is to review the surface contamination of three hazardous drugs within a teaching hospital and comment the different strategies put in place over the years in the context of these multicenter studies. Background: Many cross-sectional studies have been published about surface contamination with hazardous drugs in healthcare settings. Methods: This is a descriptive retrospective and longitudinal study. The study was conducted in a 500-bed mother-child university health center in Quebec, Canada. Results: A total of 72 samples (e. g. 36 in the pharmacy and 36 in outpatient care area) were obtained between 2010 and 2016 for a total of 216 analyses (three drugs/samples tested). The proportion of positive samples was 50 % (36/72) for cyclophosphamide, 32 % (23/72) for ifosfamide and 19 % (14/72) for methotrexate. The cyclophosphamide concentrations measured varied from undetectable to 400 pg/cm Conclusion: This study shows a longitudinal perspective of the surface contamination of hazardous drugs in a teaching mother-child hospital. Every hospital should review its annual scorecard of contamination with a longitudinal perspective to minimize drug contamination. It is possible to contain surface contamination with hazardous drugs with different strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".