Initial assessment of multi-compound antineoplastic drug surface contamination in Argentinean healthcare centers: Insights into occupational exposures in South America
Bibliographic record
Abstract
Introduction Antineoplastic drug contamination can result in severe health effects for healthcare workers exposed to them. Despite the worldwide growing concern regarding these drugs and sustained monitoring efforts in developed countries, there is almost no data about surface contamination levels in Argentina, in particular, and South America, in general. Methods Antineoplastic drug contamination was measured in three Argentinean public hospitals (pharmacy and daycare center areas) by surface wiping and liquid chromatography coupled with tandem mass spectrometry. Results and Discussion Eleven drugs were detected, in 51 of 58 sampled surfaces, in variable concentrations from 0.00064 to 7.3 ng cm −2 , with cyclophosphamide, gemcitabine, and paclitaxel as the most prevalent drugs. This highly variable antineoplastic distribution reflects differences in facility layout, number of patients, antineoplastic drug use, etc., at each hospital. Values exceeding the 1 ng cm −2 threshold were detected in 13 surfaces of the two hospitals handling the largest amounts of antineoplastic drugs. The cyclophosphamide 75th percentile averaged 0.030 ng cm −2 comparable to the high values reported more than 10 years ago for developed countries, emphasizing the potential of reducing antineoplastic contamination by implementing routine monitoring and improved cleaning and handling procedures. Conclusion This study is the first survey of multi-compound surface antineoplastic contamination in Argentinean (and South American) hospitals, providing a baseline against which future studies can be compared. Widespread antineoplastic contamination has been detected on numerous surfaces, with concentrations surpassing suggested threshold exposure levels (1 ng cm −1 ) for some surfaces in two of the sampled hospitals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".