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Record W4400125530 · doi:10.1093/bjd/ljae090.151

BH04 A global survey to assess practice of laboratory testing in alopecia areata by hair specialists

2024· article· en· W4400125530 on OpenAlexfundno aff
Cathal O’Connor, Leila Asfour, Laita Bokhari, George Cotsarelis, C. Cotter, Brittany G. Craiglow, Lara Cutlar, Rachita Dhurat, Ncoza C. Dlova, Isabella Doche, Jeff Donovan, Samantha Eisman, Daniel Fernandes Melo, Matthew Harries, Maria Hordinsky, Ahmed Kazmi, Brett King, Nekma Meah, Manabu Ohyama, Julya Ovcharenko, Rodrigo Primez, Bianca Maria Piraccini, Lidia Rudnicka, David Saceda Corralo, Jerry Shapiro, Rod Sinclair, Blake E. Smith, Michela Starace, Sérgio Vañó-Galván, Kevin Lei Wang, Katherine York, Ian A. McDonald, Dmitri Wall

Bibliographic record

VenueBritish Journal of Dermatology · 2024
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsnot available
FundersChangi General HospitalNYU Grossman School of MedicineUniwersytet WarszawskiVictoria UniversityUniversità di BolognaUniversidade de São PauloUniversity of PennsylvaniaInyuvesi Yakwazulu-NataliYork UniversityWarszawski Uniwersytet MedycznyUniversity of MinnesotaYale University
KeywordsAlopecia areataMedicineHair lossDermatologySubspecialtyDiseaseAlopecia universalisThyroid diseaseFamily medicinePediatricsInternal medicineThyroid

Abstract

fetched live from OpenAlex

Abstract Alopecia areata (AA) is a common, immune-mediated, nonscarring alopecia associated with increased risk of other autoimmune conditions such as thyroid disease. Risk of cardiovascular disease in AA is contentious, but is relevant in the era of Janus kinase (JAK) inhibitor therapy. Rarely, nutritional deficiency and infective disorders, such as syphilis, can mimic AA. International guidelines, including those issued by the British Association of Dermatologists, recommend that investigations are unnecessary when patients are asymptomatic for other conditions. Despite guidelines, there is diversity among experts in AA with respect to laboratory investigation, which this study aimed to describe. Thirty dermatologists specializing in hair disorders from 14 countries and six continents contributed to development of a survey to investigate variation in expert practice regarding laboratory testing in AA. The survey was distributed via Google Forms among expert hair networks globally. Of 130 dermatologists, 73.1% (n = 95) had a subspecialty interest in hair disorders. Almost all (87.7%, n = 114) saw both children and adults with AA. There was a global spread, with 41.5% (n = 54) from Europe, 23.1% (n = 30) from Asia and 13.8% (n = 18) from Africa. For one-quarter (26.4%) of respondents, hair loss disorders represented > 50% of their patients. Almost two-thirds (63.8%) did not perform anthropometry, while 66.2% used the Severity of Alopecia Tool and 39.2% used the Dermatology Life Quality Index. Over half (51.5%) routinely or always performed screening bloods for coexisting autoimmune illness (e.g. thyroid disease or coeliac disease), 39.2% routinely performed screening bloods for contributory conditions (e.g. nutritional deficiencies or endocrine disease), and 18.6% routinely screen for alternative diagnoses (e.g. syphilis) in all patients with AA. Regarding the identification of comorbidities, 70.8% routinely ordered thyroid testing, 65.4% ordered full blood counts, and 46.9% requested liver function testing. Before starting conventional systemic therapy (e.g. ciclosporin or methotrexate), 80.8% ordered full blood counts, 79.2% liver function, 73.8% renal function, 66.7% hepatitis B and C serology, 57.7% HIV testing, and 53.1% lipid testing. Before starting JAK inhibitors, 89.8% ordered full blood counts, 88% liver function tests, 73.8% renal function tests, 67.7% hepatitis B and C serology, 65.4% tuberculosis testing, 62.3% lipid testing, and 56.2% HIV testing. Three-monthly monitoring was conducted by 59.4%. This study identifies that real-world practice among AA experts (who may see more severe or complex AA) is variable with respect to laboratory testing, and that a renewed discussion is warranted in this regard.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.336
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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