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Record W1995976470 · doi:10.1097/dss.0000000000000324

Effect of 27-MHz Radiofrequency on Hair Follicles

2015· article· en· W1995976470 on OpenAlexaff
Dong Hyun Kim, Amélie Lavoie, Gilles Ratté, Clément Beaumont, Lucie Germain, Danielle Larouche

Bibliographic record

VenueDermatologic Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHair follicleScalpHair transplantationAnatomyHair lossMedicineDermal papillaeHair removalHair shaftSurgeryPathologyDermatologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: A multitude of methods and treatments exist for cosmetic hair removal. Electroepilation is a commonly performed method of hair removal that is so-called "permanent"; however, there is a paucity of histological studies of the effects of radiofrequency (RF) on hair follicles. OBJECTIVE: This study aimed to observe the destruction of human hair follicles and surrounding tissue after the treatment with 27.12-MHz RF, with more attention paid to the thermal destruction of bulge and bulb/dermal papilla. METHODS: Human scalp specimens obtained during face-lift surgery were treated with 27.12-MHz RF. The probe tip was inserted into hair follicle, RF current was applied, and treated specimens were processed for histological analysis. RESULTS: Significant damages were observed on treated hair follicles. Thermal damage was lance-shaped and extended over several hundred micrometers (100-400 μm). The location of destruction areas varied, likely depending on the point of insertion of the probe. The epidermis remained intact. CONCLUSION: This study shows that the general mechanism of thermolysis is to generate damage to cells and tissues surrounding the insertion point of the filament. The results suggest that if the insertion point is close to the bulge region, there is a risk to destroy hair follicle epithelial stem cells.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.033
GPT teacher head0.286
Teacher spread0.253 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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