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Record W2130552922 · doi:10.2310/7750.2014.13212

Progression of Surgical Hair Restoration Techniques

2015· review· en· W2130552922 on OpenAlexaff
Aditya Gupta, Danika C.A. Lyons, Deanne Daigle

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2015
Typereview
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsMediprobe Research (Canada)University of Toronto
Fundersnot available
KeywordsMedicineHair follicleHair transplantationScalpScarring alopeciaRegeneration (biology)SurgeryInternal medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Early surgical hair restoration (SHR) techniques were fraught with limitations. Major advancements and refinements have taken place yielding minimally invasive, relatively scar-free, and natural-looking hair transplantations. OBJECTIVE: Our aim was to review the origins and advancements of SHR and to discuss future directions for the field. METHODS: Searches were performed using: Pubmed, Scopus, and the International Society of Hair Restoration Surgery's Hair Transplant Forum International for articles related to SHR. Reference sections of articles obtained were reviewed. Relevant textbooks obtained were reviewed. RESULTS AND CONCLUSION: SHR techniques originated as macro-level graft transplantations and excision of scalp tissue. They progressed toward micro-level graft transplantations performed with extreme caution and precision. However, all SHR techniques are limited by their reliance on existing donor hair to fill balding areas. Further advancements in hair follicle cell cloning and regeneration of growth may offer a solution to this overarching limitation.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.394
Teacher spread0.320 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2015
Admission routes1
Has abstractyes

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