Evaluation of portable ultrasound machine contamination in a community emergency department
Bibliographic record
Abstract
Objectives: The aim of this study was to explore the gross contamination rate of a portable ultrasound (US) machine in a community Emergency Department (ED) and to examine whether there is an association between the time of the day and the frequency of contamination.Methods: A total of 61 photographic samples of the US machine were collected over 23 days to capture visible contamination.Collection times were evenly distributed over three blocks of time: day, evening, and night.Each sample consisted of six photos of the US machine and were categorized into three groups: (1) transducers, (2) touch screen, and (3) other areas.Samples were assessed for contamination on a three-point scale by three independent reviewers.Descriptive statistics and Chi Square test were used to describe the frequency of contamination, and relationship between time of day and frequency of contamination, respectively.Results: The transducers were contaminated with blood and body fluid in 2/62 (4%) and ultrasound gel in 52/61 (85%) samples.Gel contamination was found on the touchscreen in 52 (85%) samples, and 42 (69%) samples on the other areas.No significant association between time of day and contamination was found. Conclusions:The findings of this study demonstrate various levels of gross visual contamination of the sole ultrasound machine in a community emergency department.We feel that this study provides a foundation for the development of local QI processes for US decontamination procedures as well as a platform for knowledge translation and future study.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".