MétaCan
Menu
Back to cohort
Record W4318574504 · doi:10.1097/dss.0000000000003701

Does Topical Anesthesia Alter the Outcomes of Vascular Laser Procedures? Review of Vasodynamic Effects and Clinical Outcomes Data

2023· article· en· W4318574504 on OpenAlexaff
Caitlyn Glover, Vincent Richer

Bibliographic record

VenueDermatologic Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineTopical anestheticTopical anesthesiaAnestheticContext (archaeology)AnesthesiaIntense pulsed lightMEDLINEDermatology

Abstract

fetched live from OpenAlex

BACKGROUND: Topical anesthesia has vasodynamic effects within the skin and therefore has the potential to change the presence of hemoglobin as a chromophore before intense pulsed light (IPL) and vascular laser treatments. It is unclear whether this is clinically relevant. Global consensus on the use of topical anesthetics in this context is lacking. OBJECTIVE: Review the effects of topical anesthetics on the skin microvasculature and the clinical implications of such effects on vascular treatments. METHODS: PubMed and Medline searches were performed to identify studies examining the vasodynamic effects of topical anesthesia on skin and evaluating differences in efficacy of IPL and vascular laser treatments with or without topical anesthetic use. RESULTS: Published studies reveal variable effects of different topical anesthetic agents on skin microvasculature. Only 3 controlled studies that directly examined the effect of topical anesthesia on clinical outcomes for pulsed dye laser (PDL) treatment of vascular conditions were identified. They did not support a difference in clinical outcomes with or without the use of topical anesthesia before PDL treatment. CONCLUSION: Although topical anesthetic agents have vasodynamic effects within the skin, there is currently insufficient evidence to advise against their use before light and laser-based vascular treatments.

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)0.003
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 score0.038
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.062
GPT teacher head0.388
Teacher spread0.326 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDermatologic SurgerySame topicDermatologic Treatments and ResearchFrench-language works237,207