EMLA and Ear Surgery: Is It Possible to Achieve Full-Thickness Anesthesia With EMLA?
Bibliographic record
Abstract
BACKGROUND: Topical local anesthetic applications offer painless, effective analgesia with slow onset but prolonged duration and minimal side effects. EMLA (Eczacibasi Pharmaceuticals, Istanbul, Turkey) is the most universally used topical local anesthetic. OBJECTIVE: The aim of this prospective, randomized, double-blind study is to evaluate the efficacy of EMLA on total anesthesia of the external ear. METHODS: Twenty-two patients with helical lesions were divided into two groups. Group A received EMLA on both the anterior and posterior surfaces of the ear, and group B received EMLA on only one side of the ear. After 120 minutes of occlusive dressing, the surgery was performed. The short form of the McGill Pain Questionnaire and a numerical visual analog scale were used to measure overall pain quality and intensity during and at the end of surgery. RESULTS: Visual analog scale scores (four for group A and six for group B6) between two groups using Student's t-test (p=0210) and concerning McGill Pain Questionnaire scores with Wilcoxon signed ranks test (p=0.058) between two groups showed no statistical significant difference. CONCLUSION: It seems that EMLA cream is not a good and first option for achieving full anesthesia on the ear because of its poor anesthetic effect. We do not consider EMLA cream to be clinically useful for major surgical attempt on the ear.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".