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Record W4405192173 · doi:10.25259/jcas_3_24

Efficacy and safety of nanofractional radiofrequency in treatment of atrophic acne scars: A retrospective analysis of 5 years

2024· article· en· W4405192173 on OpenAlexaboutno aff
Ramandeep Kaur, Seema Sood, Ishan Agrawal, Arunima Ray

Bibliographic record

VenueJournal of Cutaneous and Aesthetic Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineErythemaScarsDermatologyAcneAcne scarsRetrospective cohort studyBurning SensationPatient satisfactionSurgery

Abstract

fetched live from OpenAlex

Objectives: The objective of the study was to evaluate the efficacy of nanofractional radiofrequency in the treatment of acne scars. Material and Methods: In this 5-year retrospective study, adults with atrophic acne scars on their cheeks underwent four monthly sessions of nanofractional radiofrequency treatment (Venus Viva™, Venus Concept Inc., Toronto, Canada). Follow-up occurred 2 months after the last session. Clinical photographs were assessed by physicians and patients, and two dermatologists performed independent subjective analysis. Side effects, including pain, erythema, post-inflammatory pigmentation, and burning, were recorded after each session. Results: In the analysis, 65 patients were included, with a mean age of 27.6 ± 5.6 years. Among them, 67.7% had Fitzpatrick skin type IV. The mean satisfaction score at the end of the study was 7.33 ± 1.31, and 55.4% of patients scored >7. Of the 24 patients with scars lasting less than 6 months, 70.8% experienced >75% improvement. For patients with macular scars (11 in total), 72.7% saw >75% improvement. Transient pain and swelling were observed in all patients, while 32 out of 65 reported a burning sensation lasting <2 h. Conclusion: Nanofractional radiofrequency is highly effective, with positive responses in macular to mild scars. Scar duration is inversely related to treatment response. It is safe with transient, controlled side effects.

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 score0.381
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.299
Teacher spread0.281 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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