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Record W2029348832 · doi:10.1097/gox.0000000000000106

Discomfort during Periorbital and Lateral Temporal Laser Vein Treatment

2014· article· en· W2029348832 on OpenAlexaff
James P. Bonaparte, David E. Ellis

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2014
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineVisual analogue scaleLidocainePlaceboSurgeryVeinAnesthesia

Abstract

fetched live from OpenAlex

BACKGROUND: Treatments for cosmetically unpleasing periocular and lateral temporal veins are limited. The purpose of this study was to test the hypothesis that the application of topical lidocaine before the cosmetic treatment of periorbital and lateral temporal veins with a neodymium-doped yttrium aluminum garnet (ND:YAG) laser will result in a significant reduction in subjective pain compared with placebo as assessed using a visual analogue scale. METHODS: Twenty patients who required bilateral treatment of facial veins were randomly assigned to receive either placebo or 30% lidocaine gel applied topically over the veins, a split-body design. Both the investigator and the patient were blinded to the treatment. An ND:YAG laser was used to treat the veins. Patients completed a visual analogue scale to assess the pain on each side of the face. Data were analyzed using nonparametric data testing. RESULTS: There was a 64.0% reduction in pain on the treatment side compared with the placebo side (P < 0.001). There was no significant difference in patient-assessed subjective efficacy between sides (P = 0.2). Complications were minimal and mild. CONCLUSIONS: Patients undergoing periorbital and temporal vein ablation using ND:YAG laser should be offered topical lidocaine as the pain levels are moderate. The use of topical 30% lidocaine results in a significant reduction in pain levels.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.025
GPT teacher head0.300
Teacher spread0.275 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2014
Admission routes1
Has abstractyes

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