MétaCan
Menu
Back to cohort
Record W3128851726 · doi:10.31838/srp.2021.3.20

Combined Fractional Erbuim-YAG Laser With Botulinum Toxin-A Versus Botulinum Toxin-A Alone For The Treatment Of Hypertrophic Scars And Keloids

2021· article· en· W3128851726 on OpenAlexaboutno aff
Seif Allah Mohamed Elfiky, Hisham Shokeir, Mahmoud S. Elbasiouny, Nevien Samy

Bibliographic record

VenueSystematic Reviews in Pharmacy · 2021
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAblative caseBotulinum toxinScarsHypertrophic scarLaser treatmentRegimenHypertrophic scarsSurgeryLesionDermatologyTreatment modalityLaserRadiation therapy

Abstract

fetched live from OpenAlex

Background: The treatment of hypertrophic scars (HTS) and keloids remains a challenge. Not all treatment modalities have been adequately tested. Objectives: We aimed to compare the efficacy between combined fractional Er:YAG with intra-lesional botulinum toxin (Botox) and intra-lesional Botox as a monotherapy for the treatment of HTS and keloids. Patients and methods: Thirty patients with HTS and keloids were treated by intra-lesional injection of Botulinum Toxin Type –A (Botox) as a monotherapy and Botox combined with ablative fractional Er:YAG laser. Each lesion was divided into two parts. The allocation of treatment method was randomly selected. One part was treated with Botox intra-lesionally 5 IU/cm2. The other part was subjected to combined intra-lesional Botox and ablative fractional Er:YAG laser (2,940 nm) sessions (4 sessions every 4 weeks). Evaluation of the treatment outcomes was done by the Vancouver Scar Scale (VSS), clinical imaging, and immuno-histochemical studies. Results: There was a significant decline in VSS after treatment with the combined regimen compared to the sites treated with botox injection only (P

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.103
GPT teacher head0.394
Teacher spread0.290 · 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 designSystematic review
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueSystematic Reviews in PharmacySame topicDermatologic Treatments and ResearchFrench-language works237,207