MétaCan
Menu
Back to cohort
Record W3128467948 · doi:10.1111/ced.14597

Patient perspectives of the cumulative life course impairment of alopecia areata

2021· letter· en· W3128467948 on OpenAlexaboutno aff
Laura Burns, Kelly E. Flanagan, James T. Pathoulas, Abby Ellison, Natasha Atanaskova Mesinkovska, Maryanne M. Senna

Bibliographic record

VenueClinical and Experimental Dermatology · 2021
Typeletter
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsnot available
FundersNational Alopecia Areata Foundation
KeywordsAlopecia areataMedicineDermatologyLife course approachPediatricsPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Alopecia areata (AA) is an unpredictable form of inflammatory hair loss often viewed as a cosmetic condition, rather than a medical disease.1, 2 This perception prevails despite substantial evidence illustrating the negative effects of AA on quality of life and its associated psychological, social and financial burdens. The Cumulative Life Course Impairment (CLCI) model is a relatively new approach to characterizing these compounding disease effects. First proposed by Kimball et al. in 2010, the CLCI concept serves as a proxy for longitudinal data and aims to describe why some patients fail to achieve their ‘full life potential’.3 Previously, cross‐sectional data were compiled to describe the culmination of stigmatization, physical and psychological comorbidities, social and economic implications, and coping strategies in AA (Fig. 1).4 As a supplement, we interviewed four patients with moderate to severe AA. Each patient case is briefly summarized below and aligned with the CLCI model and current literature in Table S1. By exploring the CLCI through the patient lens, we hope to capture what many practitioners understand intuitively – that the effects of AA are broad and often long‐lasting. We aim to reinforce the importance of the model as a potential tool in identifying at‐risk patients, promoting early intervention and expanding access to medically necessary treatments. Components of the Cumulative Life Course Impairment (CLCI) model applied to alopecia areata (AA). Patient 1 was a 29‐year‐old woman with abrupt onset of alopecia universalis (AU) following a severe allergic reaction at 26 years of age. She was successfully treated with oral Janus kinase (JAK) inhibitors, which was fortunately covered by a pharmaceutical patient assistant programme. The unpredictability of AA and her fears of returning depression, anxiety and suicidal ideation have prevented her discontinuation of the medication in order to conceive, given the uncertain safety profile of this drug in pregnancy. The patient’s story is told in our related article.5 Patient 2 was a 14‐year‐old boy who developed patchy AA at the age of 12 years, and the condition abruptly progressed to alopecia totalis with concurrent facial vitiligo during high school. The patient’s medical insurance denied coverage for nearly all treatments other than corticosteroids and his family is burdened by medical costs, which they pay in instalments. Once an outgoing athlete, the patient became socially withdrawn, enduring bullying, quitting sports teams and staying behind a grade in high school. Patient 3 first experienced AA at the age of 19 years, which progressed to 90% scalp hair loss. As a high schooler, he felt stripped of his identity, bound to glasses and hats for camouflage. He was fearful of social situations where he could not hide his hair loss, avoiding pools, beaches and sports. He has remained unresponsive to JAK inhibitors, which were procured from Canada to decrease out‐of‐pocket costs. Patient 4 was 4 years old when her mother noticed patches of hair loss. The condition resumed when the patient was in middle school, and progressed to AU when she was in college. Now a successful physician, the patient has learned to embrace AA as part of her identity, frequently encountering the misperception that she does not struggle with her hair loss. Despite the patient’s strong coping skills, the disease continues to bring curious looks, hurtful comments about her appearance and worry about any future children inheriting an autoimmune disease. Patient experiences with AA are highly variable, individualized and challenging to characterize with traditional cross‐sectional studies and surveys. These case reports illustrate the broad effects of AA based on severity, timing, access to treatments, financial resources and social support. All of the patients interviewed believe that their lives would have followed a different course if they had never been affected by AA. Our hope is these narratives, in combination with the CLCI model, demonstrate that regardless of current quality of life assessment, AA has the potential to positively and negatively alter overall life trajectory. This project was partly funded by a National Alopecia Areata Foundation medical student research award. Conflict of interest: the authors declare that they have no conflicts of interest. Linked article: Burns LJ et al. Alopecia universalis: a patient’s perspective of the cumulative life course impairment. Clin Exp Dermatol 2021. Tab S1. Experiences described in patient narratives supported by cross‐sectional evidence of the Cumulative Life Course Impairment (CLCI) model.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.350
Teacher spread0.320 · 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 designQualitative
Domainnot available
GenreCommentary

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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueClinical and Experimental DermatologySame topicHair Growth and DisordersFrench-language works237,207