Impact of a disinfecting technique on microbial contamination of computer keyboards and telephone handsets
Bibliographic record
Abstract
Background: Computers and telephones are ubiquitous in the hospital and have been shown to be contaminated with potentially pathogenic microorganism. The aim of the study was to determine microbial contamination on computer keyboards and telephone handsets and the effectiveness of a disinfecting technique (DT). Methods: A matched cross-over study, involving an overall of 50 computer keyboards and 50 telephone handsets, was conducted in three hospitals, located in the Local Health Area of Siena (Italy) before and after the use of an innovative DT consisting of a malleable-elastic compound, containing ethanol, which adheres to surfaces, removes dirt and disinfects. Total bacterial count was evaluated and several types of bacteria and fungi were researched pre- and post- use of the DT. Non parametric tests: Wilcoxon signed rank and Mann Whitney, were used, when appropriate. Results: The DT was effective in disinfecting the objects. In fact, Colony-Forming Units (CFUs) decreased to zero in most comparisons. All the comparisons showed significant differences (p<0.001) after the DT, both for computer keyboards and telephone handsets. The only exception was found for molds in telephones (p=0.062). Keyboards emerged to be dirtier than telephones handsets (p<0.05) for several types of bacteria. Conclusions: The data suggest that microbial contamination of keyboards and telephones is considerable. These objects can be a vehicle for Health Care-Associated Infections HAIs and their disinfection should not be neglected. The DT showed to be appropriate for the disinfecting purpose.
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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 | 0.003 |
| 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 | 0.001 | 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".