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EMLA and Ear Surgery: Is It Possible to Achieve Full-Thickness Anesthesia With EMLA?

2004· article· en· W1964377876 on OpenAlexaboutno aff
Nedim Sarıfakioğlu, Ahmet Terzioğlu, Bülent Çığşar, Gürcan Aslan

Bibliographic record

VenueDermatologic Surgery · 2004
Typearticle
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVisual analogue scaleAnesthesiaPrilocaineLocal anestheticTopical anestheticTopical anesthesiaAnestheticLidocaineSurgeryLocal anesthesiaRandomized controlled trial

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.027
GPT teacher head0.256
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2004
Admission routes1
Has abstractyes

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