Assessing variability of antineoplastic drugs handling practices in clinical settings
Bibliographic record
Abstract
The United States Pharmacopeia (USP) Chapter <800> guidelines will be adopted in the U.S. and Canada in 2019, requiring regular surface sampling for antineoplastic drug (AD) surface contamination as a means of environmental surveillance. USP Chapter <800> does not provide guidance on when and where to sample. Research to support the development of such guidance within a broader sampling strategy is limited. This study was conducted to help address some of the underlying information gaps by identifying surfaces pharmacy and nursing staff are likely to contact, presenting a potential dermal exposure risk. Observations were conducted at one regional and one urban clinic, providing insight into inter- and intra-worker variability and between-clinic differences based on size and patient load. Thirteen surfaces in the compounding pharmacies and 14 surfaces in the patient administration were initially selected for video observations. Following a preliminary assessment to eliminate surfaces that were touched infrequently or not at all, five commonly touched surfaces in the compounding pharmacy areas (vials, syringes, IV lines, IV bags, waste bags) and six commonly touched surfaces in the patient administration area (yellow containment bag, IV bag, IV line, patient port, computer workstation) were assessed further. Variability between healthcare staff and clinics in pharmacy staff was low for both the mean frequency and duration of touch to surfaces. Differences between clinics in frequency of contact among nursing staff in patient administration areas were significant (two-way ANOVA) for five of the six surfaces. Duration of contact was not significantly different except for duration of touching the IV pump. These insights will be used to give guidance in selecting locations for a longitudinal surveillance study and help tailor worker training to reduce exposure risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".