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Record W3046333676 · doi:10.1308/rcsann.2020.0154

Double vest lipodermal flaps for depressed facial scars

2020· article· en· W3046333676 on OpenAlexaboutno aff
YN Qassim, AA Ali, MJ Alfeehan, WK Albayati

Bibliographic record

VenueAnnals of The Royal College of Surgeons of England · 2020
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsScarsMedicineVESTSurgeryVisual analogue scalePatient satisfaction

Abstract

fetched live from OpenAlex

INTRODUCTION: Depressed tethered scar is a common problem that can cause emotional, social and behavioural problems, especially when it involves the exposed body parts. Several techniques have been described for treating these depressed scars, but none of these can fulfil the optimal results. AIM: Evaluating the aesthetic outcome of using a double vest lipodermal flaps for treating depressed facial scars. MATERIALS AND METHODS: The study included 25 patients with depressed facial scars who underwent scar revision. Their mean age was 31 years. Under local anaesthesia, the scarred area was de-epithelialised and double dart lipodermal flaps were used for revision. Visual analogue and Vancouver scar scales were used as subjective and objective parameters of evaluation, respectively. RESULTS: All the patients followed up for five to eight months. No complications were observed during the scar healing period. Patients satisfaction according to the visual analogue scale showed an average value of 8. The mean total scale according to the Vancouver scar scale was 2.6. CONCLUSION: The new technique of using double vest lipodermal flaps is simple and offers a promising alternative for revising depressed 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.093
GPT teacher head0.329
Teacher spread0.236 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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