Antimicrobial Effect of a Single Dose of Amoxicillin on the Oral Microbiota
Bibliographic record
Abstract
PURPOSE: Amoxicillin is commonly used in oral surgery for antimicrobial prophylaxis against surgical-site infection and bacteremia because of its effect on oral streptococci. The aim of this study was to determine whether amoxicillin reaches the break-point concentrations in saliva and has any effect on the salivary microbiota, colonizing bacteria on mucosal membranes and on the gingival crevice after a single dose of amoxicillin. MATERIAL AND METHODS: Twenty subjects received 2 g of amoxicillin, per os. The facultative and strictly anaerobic microflora, as well as the streptococcal microflora specifically, were followed from baseline and after 1, 4, and 24 hours. Samples were taken for microbial analysis from saliva, the dorsum of the tongue, and the gingival crevice, and were inoculated and cultured. Plasma samples and saliva samples were analyzed for amoxicillin concentrations (free and protein bound) using liquid chromatography and mass-spectrometry. RESULTS: Amoxicillin was detected in concentrations over the break-point (>2 μg/mL) of amoxicillin in plasma after 1 and 4 hours but not after 24 hours. The dose had a significant effect on the streptococci in the gingival crevice. CONCLUSION: A single dose given as prophylaxis to prevent a surgical-site infection results in a significant reducing effect on the oral streptococcal microflora in the gingival crevice and may have an impact on bacteria spreading into tissues and the bacteremia of streptococci.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".