The effect of nail sticker use on hand surface bacterial counts after surgical scrubbing
Bibliographic record
Abstract
Objective: We hypothesized that applying nail stickers to nails shorter than 2 mm would increase bacterial load and species abundance compared to nails of the same length without stickers. The aim of our study was to investigate aerobic bacterial load and species abundance on fingernails before and after nail sticker application over a 2-week period. Methods: 50 participants were enrolled, with 48 completing the entire study. Participants wore study-provided nail stickers (solid or patterned) for 7 days. All nails were trimmed to ≤ 2 mm. Standardized surgical scrubs with 4% chlorhexidine were performed, and swabs from fingernail surfaces and subungual spaces were collected at 3 time points: day 0 (before application), day 7 (immediately after application), and day 14 (1 week after application). We quantified bacterial load, and species were identified with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Results: Median log10 bacterial loads were 1.48 at day 0, 1.00 at day 7, and 2.01 at day 14. Bacterial load increased from days 7 to 14 but did not differ between days 0 and 7 or days 0 and 14. Staphylococcus epidermidis abundance rose over time, while sticker design had no effect. Enterococcus faecalis was detected only once at day 14. Conclusions: Wearing nail stickers for 1 week significantly increased bacterial load despite surgical scrubbing, likely due to adhesive surfaces promoting bacterial adhesion. Clinical Relevance: These findings suggest that nail stickers may compromise hand hygiene and increase infection risks in veterinary clinical settings. Avoiding nail stickers during procedures may reduce bacterial persistence and potential zoonotic transmission.
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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 | 0.004 |
| 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".