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The Alopecia Areata Severity and Morbidity Index (ASAMI) Study

2024· article· en· W4391598028 on OpenAlex
Anthony Moussa, Michaela Bennett, Dmitri Wall, Nekma Meah, Katherine York, Laita Bokhari, Leila Asfour, Huw Rees, Leonardo Spagnol Abraham, Daniel Asz‐Sigall, F. Buket Basmanav, Wilma F. Bergfeld, Regina C. Betz, Bevin Bhoyrul, Ulrike Blume‐Peytavi, Valerie Callender, Vijaya Chitreddy, Andrea Combalía, George Cotsarelis, Brittany G. Craiglow, Rachita Dhurat, Jeff Donovan, Andrei G. Doroshkevich, Samantha Eisman, Paul Farrant, Juan Ferrando, Aida Gadzhigoroeva, Jack Green, Ramón Grimalt, Matthew Harries, Maria Hordinsky, Alan D. Irvine, Victoria Jolliffe, Spartak Kaiumov, Brett King, Joyce Lee, Won‐Soo Lee, Jane Li, Nino Lortkipanidze, Amy McMichael, Natasha Atanaskova Mesinkovska, Andrew G. Messenger, Paradi Mirmirani, Elise A. Olsen, Seth J. Orlow, Yuliya Ovcharenko, Bianca Maria Piraccini, Rodrigo Pirmez, Adriana Rakowska, Pascal Reygagne, Lidia Rudnicka, David Saceda Corralo, Maryanne M. Senna, Jerry Shapiro, Pooja Sharma, Tatiana Siliuk, Michela Starace, Poonkiat Suchonwanit, Anita Takwale, Antonellá Tosti, Sérgio Vañó-Galván, Willem I. Visser, Annika Vogt, Martin Wade, Leona Yip, Cheng Zhou, Rodney Sinclair

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJAMA Dermatology · 2024
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineAlopecia areataHair lossScalpDermatology Life Quality IndexAnxietyQuality of life (healthcare)Delphi methodDepression (economics)DermatologySomatizationSeverity of illnessPsychiatryFamily medicinePsoriasis

Abstract

fetched live from OpenAlex

Importance: Current measures of alopecia areata (AA) severity, such as the Severity of Alopecia Tool score, do not adequately capture overall disease impact. Objective: To explore factors associated with AA severity beyond scalp hair loss, and to support the development of the Alopecia Areata Severity and Morbidity Index (ASAMI). Evidence Review: A total of 74 hair and scalp disorder specialists from multiple continents were invited to participate in an eDelphi project consisting of 3 survey rounds. The first 2 sessions took place via a text-based web application following the Delphi study design. The final round took place virtually among participants via video conferencing software on April 30, 2022. Findings: Of all invited experts, 64 completed the first survey round (global representation: Africa [4.7%], Asia [9.4%], Australia [14.1%], Europe [43.8%], North America [23.4%], and South America [4.7%]; health care setting: public [20.3%], private [28.1%], and both [51.6%]). A total of 58 specialists completed the second round, and 42 participated in the final video conference meeting. Overall, consensus was achieved in 96 of 107 questions. Several factors, independent of the Severity of Alopecia Tool score, were identified as potentially worsening AA severity outcomes. These factors included a disease duration of 12 months or more, 3 or more relapses, inadequate response to topical or systemic treatments, rapid disease progression, difficulty in cosmetically concealing hair loss, facial hair involvement (eyebrows, eyelashes, and/or beard), nail involvement, impaired quality of life, and a history of anxiety, depression, or suicidal ideation due to or exacerbated by AA. Consensus was reached that the Alopecia Areata Investigator Global Assessment scale adequately classified the severity of scalp hair loss. Conclusions and Relevance: This eDelphi survey study, with consensus among global experts, identified various determinants of AA severity, encompassing not only scalp hair loss but also other outcomes. These findings are expected to facilitate the development of a multicomponent severity tool that endeavors to competently measure disease impact. The findings are also anticipated to aid in identifying candidates for current and emerging systemic treatments. Future research must incorporate the perspectives of patients and the public to assign weight to the domains recognized in this project as associated with AA severity.

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.

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 score0.127
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.286
Teacher spread0.272 · 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