Developing wipe sampling strategy guidance for assessing environmental contamination of antineoplastic drugs
Bibliographic record
Abstract
Surveillance for environmental contamination of antineoplastic drugs has been recommended by authoritative bodies such as the United States Pharmacopeia and the National Association of Pharmacy Regulatory Authorities. Clear guidance is needed on how to develop sampling strategies that align with surveillance objectives efficiently and effectively. We conducted a series of simulations using previously collected surveillance data from nine cancer treatment centers to evaluate different sampling strategies. We evaluated the impact of sampling 2, 5, 10, or 20 surfaces, at monthly, quarterly, semi-annual, and annual frequencies, while employing either a random or sentinel surface selection strategy to assess contamination by a single antineoplastic drug (AD) or by a panel of three ADs. We applied two different benchmarks: a binary benchmark of above or below the limit of detection and AD-specific hygienic guidance values, based on 90th percentile values as quantitative benchmarks. The use of sentinel surfaces to evaluate a three-drug panel relative to 90th percentile hygienic guidance values (HGVs) resulted in the most efficient and effective surveillance strategy.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".