Assessment of chemical contamination by cancer drugs during use of the RIVA <sup>TM</sup> compounding robot: A pilot study
Bibliographic record
Abstract
Introduction Many hospitals are now investing in robotic compounding system for the preparation of cytotoxic agents. The objective of the present study was to describe contamination by cytotoxics inside and outside the RIVA TM robot (ARxIUM, Winnipeg, Canada). Material & Methods We applied a risk analysis to determine which locations inside and outside the compounding robot should be monitored. Samples were collected by swabbing with a wet swab (using 0.1 mL of sterile water) before the robots was cleaned. Ten cytotoxics compounded with the robot were screened for using LC-MS/MS. We determined the percentage contamination rates inside (CR in ) and outside (CR out ) the robot and the amounts of each contaminant (in ng/cm²). If a sample was found to be positive, a corrective action was implemented. Results Our risk analysis highlighted 10 locations inside the robot and 7 outside. Ten sampling campaigns (10 samples per campaign) were performed. The mean CR in (40%) was significantly higher than the mean CR out (2%; p < 10 −4 ). Gemcitabine and cyclophosphamide were the main contaminants. After the implementation of corrective measures (such as daily cleaning with SDS/isopropyl alcohol), the CR in fell from 60% to 10%. Discussion/conclusion The frequency of contamination was lower for robotic compounding than for manual compounding in an isolator. However, robotic compounding tended to generated larger mean amounts of contaminant; this was related to incidents such as splashing when syringes were disposed of after the compounding. The implementation of corrective actions effectively reduced the CRs. Further longer-term studies are required to confirm these results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".