Effect of Lidocaine and Epinephrine on Staphylococcus aureus in a Guinea Pig Model of Surgical Wound Infection
Bibliographic record
Abstract
Postoperative wound infection, most often with, is of ubiquitous concern in surgical practice, occurring in an average of 1.5 to 5 percent of all procedures. The antimicrobial properties of local anesthetics have been documented over the past 25 years by in vitro studies. This study evaluates the effects of lidocaine preparations on in an in vivo setting. In a wound infection model using live albino guinea pigs, inoculum was introduced for the reproducible bacterial colonization of clean surgical wounds. One of two sites on the dorsum of each animal was infiltrated with a commercial lidocaine preparation (with and without epinephrine) prior to inoculation with (10 cfu/ml). The other site, inoculated with without preinfiltration with lidocaine, served as the control. Cultures from the sites treated with lidocaine were then compared with cultures from the control sites. All control sites had a consistent presence >or=10 cfu/ml, the threshold for bacterial inhibition of wound healing. Infiltration of the wound with 2 ml of 2% lidocaine prior to inoculation was associated with an average decrease in bacterial count of >70 percent ( n= 19). On the other hand, the addition of epinephrine (1:100,000) to lidocaine was associated with a 20-fold in bacterial counts compared with control values ( n= 10). This is the first study to demonstrate inhibition of by a local anesthetic agent in an in vivo model of a surgical wound. This information suggests a possible role for local anesthetics in prophylaxis against surgical wound infection.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".