Pilot Study Evaluating the Efficacy of Four Cleaning Solutions and Two Types of Mops in Delimited Areas of a Floor Contaminated with Cyclophosphamide
Bibliographic record
Abstract
Abstract Background Hazardous drugs (HD) traces are measured in most hospitals that perform environmental surveillance. Uncertainties exist regarding the cleaning agents and procedures needed to completely remove HD contamination The objective was to evaluate the efficacy of four cleaning solutions and two types of mops in reducing contamination on a floor contaminated with a predetermined amount of cyclophosphamide (CP). Methods This pilot study was divided into three steps: 1) the voluntary contamination of a pre-delimited area on the floor (3600cm Results The average decontamination efficacy of the four cleaning products used was: 99.53%±0.41 % for the detergent, 99.74%±0.15 % for quaternary ammonium, 99.86%±0.11 % for sodium hypochlorite, 99.75%±0.15 %, for hydrogen peroxide. The average decontamination efficacy for disposable mops was 99.58%±0.28 % and 99.86%±0.09 % for non-disposable mops. Conclusion Sodium hypochlorite, hydrogen peroxide, quaternary ammonium and a detergent applied with a disposable or a non-disposable mop were efficient to reduce the CP concentration on the floor contaminated with a predetermined quantity of CP. However, no cleaning scenarios was able to remove 100 % of CP after one cleaning session. Further studies are required to identify an optimal 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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".