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Record W2485118769 · doi:10.4103/0974-7753.188033

Female pattern hair loss: A retrospective study in a tertiary referral center

2016· article· en· W2485118769 on OpenAlexaff
TeeWei Siah, Llorenia Muir-Green, Jerry Shapiro

Bibliographic record

VenueInternational Journal of Trichology · 2016
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHair lossMedicinePediatricsFamily historyReferralMedical historyRetrospective cohort studyMedical recordDermatologyInternal medicineFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Female pattern hair loss (FPHL) is a very common problem in women. The underlying pathophysiology remains unclear, and there are no universally agreed treatment guidelines. OBJECTIVE: We explored the clinical features, relevant medical and family history, laboratory evaluation, and treatment and compliance of 210 patients with FPHL. METHODS: Data analysis from case notes was performed on 210 patients with a diagnosis of FPHL seen from January 2011 to December 2011. RESULTS: The youngest individual was 8 years old and the oldest was 86 years old. Nearly, 85% of the patients had a family history of androgenetic alopecia. Hypothyroidism and hypertension are the most common medical problems. Telogen effluvium (TE) is the most common concurrent hair loss condition. Only 38% of the patients were found to have normal Vitamin D level, 71% had ferritin level above 30 μg/L, and 85% had normal zinc level at the first consultation. Fifty-nine percent of the patients failed to attend any follow-up appointments. LIMITATIONS: One of the limitations of this study is its retrospective nature. Moreover, the severity of FPHL in terms of Ludwig score was not routinely documented in the medical charts. CONCLUSION: History of TE, hypothyroidism and hypertension, and low serum Vitamin D is common in our patients with FPHL.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.332
Teacher spread0.312 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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