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Record W4296156605 · doi:10.53730/ijhs.v6ns7.12788

A comparative study of effectiveness of microneedling with and without topical corticosteroids in post-burn hypertrophic scars of face and neck

2022· article· en· W4296156605 on OpenAlexaboutno aff
Mostafa Shehata, Mohamed Mahmoud Elshazly, Shaimaa Mohamed Mouneer Bebars, Mohamed Eloteify

Bibliographic record

VenueInternational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsHypertrophic scarSurgeryHypertrophic scarsDermatology

Abstract

fetched live from OpenAlex

Background: The standard treatment of post-burns scars has unsatisfactory outcomes and required several treatments. Objective: To evaluate efficacy of the microneedling with or without topical steroids on treatment of post-burn hypertrophic scars in face and neck. Patients and Methods: We included patients with post-burn hypertrophic scar of face and neck, caused by burn within the 1st year after burn, we excluded patients with coagulation defects. Patients were divided into 3 groups; Group A: microneedling once/month for 5 months, Group B: microneedling with topical steroids once/month for 5 months, and Group C: control group for just conservative treatment. Histopathological study was used for evaluation. Results: we included 60 participants; the mean age was 20 ± 9 years. After 3 & 6 months microneedling significantly decrease the Vancouver scar scale (VSS), and adding steroids significantly improve the results. Microneedling group significantly decreased the VSS after 3 and 6 months. Moreover, adding steroids significantly improved the results. Histopathologicaly, after 6 months, there was statistically difference between the three groups in thickness (p= <0.001), Nodules (p= 0.02) and inflammation (p= 0.02) of the scar. Conclusion: Microneedling with or without topical steroids found to improve the outcomes of post-burn hypertrophic scars.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.101

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.051
GPT teacher head0.410
Teacher spread0.359 · 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
Published2022
Admission routes1
Has abstractyes

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