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Record W3183540132 · doi:10.21608/aimj.2021.81579.1507

Role of nanofat injection in treating post-traumatic scars

2021· article· en· W3183540132 on OpenAlexaboutno aff
Mahmoud A. Rageh, Mohamed El‐Khalawany, Shady M. Ibrahim

Bibliographic record

VenueAl-Azhar International Medical Journal /Al-Azhar International Medical Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsScarsMedicineSurgery

Abstract

fetched live from OpenAlex

Background: Scars are the common and unpleasing result that occur following injuries of different causes. They have a great impact on the affected subjects both physically and psychologically. Aim: The aim of this study was to evaluate the role of autologous nanofat injection in refining the esthetic appearance of post-traumatic scars, along with pathological correlation of the results. Patients and methods: Nineteen patients with post-traumatic scars were treated with a single session of nanofat injection. The results were assessed after 6 months from the session using Vancouver scar scale (VSS) in addition to pathological evaluation via image analyzing system. Results: The age ranged between 19 and 40 years old. Statistical significant improvement on the VSS was noted regarding the height and pigmentation of the treated scars. On histopathological evaluation, there was a high statistical significant increase regarding epidermal thickness, collagen fibers, elastic fibers, and vascularity. Conclusion: Nanofat injection is a potential efficient therapeutic modality for post-traumatic 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)0.003
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0250.000

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 head0.360
Teacher spread0.342 · 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 designOther design
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

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