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Record W4410697464 · doi:10.1002/jvc2.70067

COLLAB: A Global Survey of Clinical and Laboratory Assessment in Alopecia Areata by Hair Specialists

2025· article· en· W4410697464 on OpenAlexafffund
Cathal O’Connor, Aoife Boyle, Leila Asfour, Bevin Bhoyrul, Laita Bokhari, George Cotsarelis, C. Cotter, Brittany G. Craiglow, Lara Cutlar, Rachita Dhurat, Ncoza C. Dlova, Isabella Doche, Jeffrey Donovan, Aaron M. Drucker, Samantha Eisman, Daniel Fernandes Melo, Matthew Harries, Maria Hordinsky, Ahmed Kazmi, Brett King, Antonios G.A. Kolios, Nekma Meah, Paradi Mirmirani, Arash Mostaghimi, Manabu Ohyama, Yuliya Ovcharenko, Rodrigo Pirmez, Bianca Maria Piraccini, Lidia Rudnicka, David Saceda‐Corralo, Jerry Shapiro, Cathryn Sibbald, Rodney Sinclair, Blake R. C. Smith, Michela Starace, Sérgio Vañó-Galván, Wooseok Koh, Katherine York, Ian McDonald, Dmitri Wall

Bibliographic record

VenueJEADV Clinical Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsSickKids FoundationHospital for Sick ChildrenWomen's College HospitalUniversity of TorontoUniversity of British Columbia
FundersNational Institute for Health and Care ResearchHealth Service ExecutiveWellcome TrustManchester Biomedical Research CentreCanadian Institute for Theoretical Astrophysics
KeywordsAlopecia areataDermatologyMedicine

Abstract

fetched live from OpenAlex

ABSTRACT Background Alopecia areata (AA) is a common non‐scarring alopecia. Data continue to emerge on associations with autoimmune and other conditions. Janus kinase inhibitors (JAKi) are increasingly used to treat AA. Objectives The aim was to assess variation in laboratory testing in patients with AA among hair experts internationally and to compare subspecialized clinical practice to current guidelines. Methods Thirty hair experts from 14 countries and six continents contributed to develop a 24‐item survey collecting demographic information on respondents; methods of severity assessment; and laboratory testing practices in AA for mimics, contributory factors, associations, and workup for systemic therapy. The survey was distributed to a global network of expert hair specialists. Results Of 214 respondents, 79.9% (171/214) had special interest/expertise in hair loss disorders, and 35.5% ( n = 76) were based in Europe. Most cared for both adults and children (87.9%, n = 188). For clinical assessment, almost two‐thirds (63.6%, n = 136) used the Severity of Alopecia Tool and 38% ( n = 84) used the Dermatology Life Quality Index. Only 24.3% ( n = 52) typically tested for alternative infectious or inflammatory diagnoses, 39.7% ( n = 85) typically tested for contributory conditions such as nutritional deficiencies, and 50.9% ( n = 109) typically tested for co‐existent autoimmune illnesses. Thyroid function testing was routinely performed in 73.4% ( n = 157) and complete blood count (CBC) was checked in 65.9% ( n = 141). Compared to conventional systemic therapy, experts were more likely to check lipid levels, creatine kinase, coagulation profiles, thrombophilia screens, tuberculosis blood testing, hepatitis B and C serology before prescribing JAKi. Conclusions Real world practice of laboratory testing for AA by hair experts, who may see more severe or complex alopecia, is variable. Most experts routinely perform thyroid function and CBC testing. We discuss evidence for indications for testing for AA mimics, contributory factors, associated autoimmune conditions, and before systemic therapy. Further research is required to characterise the role of laboratory testing in AA.

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)0.009
metaresearch head score (Gemma)0.072
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.072
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.069
GPT teacher head0.528
Teacher spread0.459 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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