Examining factors that influence the effectiveness of cleaning antineoplastic drugs from drug preparation surfaces: A pilot study
Bibliographic record
Abstract
Occupational exposure to antineoplastic drugs has been documented to result in various adverse health effects. Despite the implementation of control measures to minimize exposure, detectable levels of drug residual are still found on hospital work surfaces. Cleaning these surfaces is considered as one means to minimize the exposure potential. However, there are no consistent guiding principles related to cleaning of contaminated surfaces resulting in hospitals to adopt varying practices. As such, this pilot study sought to evaluate current cleaning protocols and identify those factors that were most effective in reducing contamination on drug preparation surfaces. Three cleaning variables were examined: (1) type of cleaning agent (CaviCide®, Phenokil II™, bleach and chlorhexidine), (2) application method of cleaning agent (directly onto surface or indirectly onto a wipe) and (3) use of isopropyl alcohol after cleaning agent application. Known concentrations of antineoplastic drugs (either methotrexate or cyclophosphamide) were placed on a stainless steel swatch and then, systematically, each of the three cleaning variables was tested. Surface wipes were collected and quantified using high-performance liquid chromatography-tandem mass spectrometry to determine the percent residual of drug remaining (with 100% being complete elimination of the drug). No one single cleaning agent proved to be effective in completely eliminating all drug contamination. The method of application had minimal effect on the amount of drug residual. In general, application of isopropyl alcohol after the use of cleaning agent further reduced the level of drug contamination although measureable levels of drug were still found in some cases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".