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Record W2765971196 · doi:10.1177/1203475417736279

Frontal Fibrosing Alopecia: Update and Review of Challenges and Successes

2017· review· en· W2765971196 on OpenAlexaff
Derek To, Jennifer Beecker

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2017
Typereview
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineScarring alopeciaDermatologyHair lossEtiologyRegimenSurgeryClinical trialIncidence (geometry)Internal medicineScalp

Abstract

fetched live from OpenAlex

BACKGROUND: Frontal fibrosing alopecia (FFA) is a variant of lichen planopilaris (LPP) and is characterised as a progressive cicatricial alopecia affecting the frontotemporal hairline. OBJECTIVES: To perform a comprehensive, up-to-date review of the etiopathogenesis, clinicopathological features, and therapeutic options for FFA. METHODS: A literature search was conducted using PubMed (from 1946) and Cochrane (from 1991) databases on March 7, 2017. We included all retrospective and prospective studies reported in English. Only cases studies with reported treatment regimen and outcome were included. No randomised control trials were found. MeSH terms used included frontal fibrosing alopecia, postmenopausal, histopathologic, cicatricial, and treatment. RESULTS: With an increasing incidence of FFA occurring predominantly in postmenopausal women, progress has been made clinically and histologically in understanding this scarring alopecia. Conflicting results have been reported with various treatments, including intralesional or oral corticosteroids, antiandrogens, antimalarials, antibiotics, and surgery. To date, no randomised control trials for treatment of FFA have been conducted. CONCLUSION: The aetiology and clinical course of FFA remain to be established. Unfortunately, despite the numerous treatment options available, no one therapeutic regimen has proven effective in stopping recession of the hairline and inducing hair growth.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.804
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.000
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 head0.112
GPT teacher head0.368
Teacher spread0.257 · 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 designOther design
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

Citations24
Published2017
Admission routes1
Has abstractyes

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