Determining the most effective common household disinfection method to reduce the microbial load on domestic dishcloths: a pilot study
Bibliographic record
Abstract
The domestic dishcloth has been shown to be the most contaminated item in the domestic kitchen, reported to contain up to 108 bacteria for up to 48 hours. Their smooth texture and large surface area allow bacteria to be transferred to kitchen surfaces easily, presenting a greater risk of cross-contamination and potentially contributing to foodborne illness. The purpose of this pilot study was to determine the most effective method to decrease the aerobic colony count (ACC) present on contaminated dishcloths. Dishcloths were inoculated in a beef slurry for 48 hours at room temperature. Contaminated dishcloths were subjected to 1-minute treatments of 10% bleach solution, lemon juice, vinegar, tap water, and microwaving. Serial dilutions were plated and incubated at 37°C overnight. Three replicates were produced, and 95% confidence intervals were calculated. Although treatments of 10% bleach solution and vinegar showed reduced ACC growth, no growth was identified after microwaving dishcloths for 1 minute on high power. There was no significant difference identified between the tap water and lemon juice treatments. Given that this is the first study conducted directly comparing different disinfection methods for dishcloths, microwaving dishcloths on high power for 1 minute can be recommended to disinfect domestic dishcloths and reduce cross-contamination within the home.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".