Disinfection efficacy studies on three different disinfection methods in health care facilities by ATP method
Bibliographic record
Abstract
Objectives: Nosocomial infection has always been a significant topic in the field of public health. The disinfection procedures involved in health care facilities are extremely important to prevent potential transmission of diseases. Therefore, this study was performed to compare the disinfection efficacy between three different disinfection methods (Accel wipes, Hubscrub industrial washer, and Steam vapor) on three pieces of non-critical medical equipment: wheelchairs, mattresses and bath chairs. Methods: The method used to evaluate the disinfection efficacy compared the reduction of contaminants count in the relative light unit using ATP monitoring methods. 30 samples of each of the three types of medical equipment were swabbed pre-disinfection and post-disinfection using the three disinfection methods. The recorded reduction number was then converted using log transformation. Statistical analysis was conducted using NCSS to assess differences between the disinfection methods. Results: The mean log-reduction of disinfection for Accel wipes, Hubscrub, and steam vapor were 1.067, 1.490, and 1.485 respectively. Steam vapor and Hubscrub displayed statistically significantly better disinfection efficacy compared to Accel wipes in terms of log reduction (overall p=0.000002). Conclusion: Hubscrub and steam vapor are better disinfectants compared to Accel wipes in terms of mean log reduction values; however, all three disinfection methods demonstrated effectiveness when cleaning and disinfecting non-critical medical equipment. For critical medical equipment, steam vapor and Hubscrub industrial washing are effective while Accel wipes do not meet the standards of high-level disinfection. As a result, combination usages of all three disinfection methods are recommended at health care facilities based on the categories of the medical equipment.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".