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Record W2975636723 · doi:10.4103/jcas.jcas_61_19

Follicular unit “exploitation”: Follicular unit extraction followed by laser hair reduction—A novel approach to repigmentation of postburn leukodermic scars

2019· article· en· W2975636723 on OpenAlexaff
Sidharth Sonthalia, Mohamed A. R. Soliman, Akmal Saad, Mohamed H.M. El-Komy, Mohamed Abdelhady, Gaurang Krishna

Bibliographic record

VenueJournal of Cutaneous and Aesthetic Surgery · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsSKiN Health
Fundersnot available
KeywordsMedicineScarsFollicular phaseDermatologyVitiligoAlopecia areataHair follicleSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Correction of depigmentation of postburn leukodermic scars is daunting. In contrast to vitiligo, the success rate of skin grafting techniques is much lower in postburn scars owing to altered skin architecture and poor graft uptake. Cellular transfer techniques such as noncultured epidermal or epidermal or keratinocyte suspension suffer the limitations of modest efficacy and high procedural cost. Follicular transplantation by the follicular unit extraction technique is being extensively used in vitiligo. Recently, it has been reported for the correction of scar alopecia. Although this approach is expected to significantly repigment pliant scars, the cosmetic unacceptability of the retained hairs may warrant removal. We describe an innovative three-step protocol to provide a repigmented smooth scar. We vernacularly labeled this technique follicular unit "exploitation," as the follicular units "exploited" positively for repigmentation were later removed by laser hair reduction.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.251
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 designBench or experimental
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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

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