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Record W2312943352 · doi:10.1155/2016/6016943

27.12 MHz Radiofrequency Ablation for Benign Cutaneous Lesions

2016· article· en· W2312943352 on OpenAlexafffund
Dong Hyun Kim, Dong Ju Hyun, Raymonde Piquette, Clément Beaumont, Lucie Germain, Danielle Larouche

Bibliographic record

VenueBioMed Research International · 2016
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsUniversité Laval
FundersNational Research Council CanadaCanadian Institutes of Health Research
KeywordsMedicineErythemaLentigoDermatologyAblationAblative caseAdverse effectCryotherapyRadiologyRadiation therapyMelanomaInternal medicine

Abstract

fetched live from OpenAlex

As surgical and/or ablative modalities, radiofrequency (RF) has been known to produce good clinical outcomes in dermatology. Recently, 27.12 MHz RF has been introduced and has several advantages over conventional 4 or 6 MHz in terms of the precise ablation and lesser pain perception. We aimed to evaluate the clinical efficacy and safety of 27.12 MHz RF for the treatment of benign cutaneous lesions. Twenty female patient subjects were enrolled. Digital photography and a USB microscope camera were used to monitor the clinical results before one session of treatment with 27.12 MHz RF and after 1 and 3 weeks. Treated lesions included telangiectasias, cherry and spider angiomas, skin tags, seborrheic keratoses, lentigo, milium, dilated pore, acne, piercing hole, and one case of neurofibroma. For vascular lesions, clinical results were excellent for 33.3%, good for 44.4%, moderate for 11.1%, and poor for 11.1%. For nonvascular lesions (epidermal lesions and other benign cutaneous lesions), clinical results were excellent for 48.3%, good for 45.2%, moderate for 3.2%, and poor for 3.2%. No serious adverse events were observed. Mild adverse events reported were slight erythema, scale, and crust. The 27.12 MHz RF treatment of benign vascular and nonvascular lesions appears safe and effective after 3 weeks of follow-up.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.156
GPT teacher head0.451
Teacher spread0.295 · 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 designNot applicable
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

Citations16
Published2016
Admission routes2
Has abstractyes

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