Evidence of exposure to cytostatic drugs in healthcare staff: a review of recent literature
Bibliographic record
Abstract
Objective: Provide updated evidence and learn about the actions that must be implemented in order to prevent the occupational exposure to cytostatic drugs. Method: A bibliographic search was carried out on the MEDLINE, COCHRANE PLUS and WEB OF SCIENCE databases, with the terms "surface contamination", "cytostatic drug", "drug preparation", "occupational exposure", "safe handling" and "closed-system transfer device", within the 2010-2015 period. Results: Thirteen articles were selected for review. These articles are from hospitals in U.S.A., Canada, Japan, Australia, Spain, Portugal and Germany. In all of them, surface contamination by cytostatic agents was found in over 15 different surfaces, with concentrations ranging from 1.69 ng/cm² to 4-784 μg/cm². The specific drugs were cyclophosphamide, ifosfamide, 5-fluorouracil, methotrexate, paclitaxel, cisplatin, gemcitabine, and docetaxel. Closed-system transfer devices can reduce the contamination in work surfaces significantly, but do not eliminate it. Conclusions: Presence of contamination by cytostatic drugs was confirmed in many hospitals across all 5 continents. In all cases, contamination was found in the cabinet, on the floor in front of the cabinet, and in other places of the Hospital Pharmacy. The drug most frequently found was cyclophosphamide. The most effective action used to reduce contamination was the closed-system transfer devices (CSTDs).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".