Efficacy of two intensive decontamination protocols and their effects after 30 days on environmental contamination by cyclophosphamide
Bibliographic record
Abstract
Abstract Objectives To evaluate the efficacy of two decontamination protocols on cyclophosphamide surface contamination and to explore its lasting effect 30 days later. Methods All sampling sites that were systematically contaminated with cyclophosphamide in 2017–2020 were included, from a convenience sample of centers. The first decontamination protocol consisted of four steps, each with 20 mL and a Wypall® wipe: detergent, sodium hypochlorite 2%, isopropyl alcohol 70% and water. The second decontamination protocol consisted of eight steps, each with 15 mL and a Micronsolo® microfibre wipe: detergent, sodium hypochlorite 2%, isopropyl alcohol 70%, water and then a second round with each of the four products. A first sampling was done at the end of a regular working day (T0), a second immediately following decontamination (T1) and a third 30 days later (T2) after regular operations. Cyclophosphamide was quantified by ultra-performance liquid chromatography – tandem mass spectrometry (limit of detection 0.001 ng/cm2). Results Seventeen sampling sites were included: six biological safety cabinet (BSC) front grilles, eight floors in front of BSCs and three cyclophosphamide storage shelves. The second protocol was more effective; however they both failed to completely remove all cyclophosphamide traces. BSCs and floors were found to be contaminated again 30 days later, at similar concentrations than at T0. A lasting effect was observed on the cyclophosphamide storage shelves that were less prone to be contaminated again. Conclusions Periodic decontamination with many cleaning steps is necessary on all surfaces, including those less frequently contaminated. Regular surface monitoring identifies systematically contaminated areas.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".