The burden of alopecia areata: A scoping review focusing on quality of life, mental health and work productivity
Bibliographic record
Abstract
Alopecia areata (AA) is a common inflammatory autoimmune disease of the hair which can have a significant negative impact on quality of life (QoL), mental health and productivity. The aim of this scoping review is to elucidate the burden of AA focusing on these three realms. Inclusion criteria included all original manuscripts with no restriction on study type or statistical method written in English (or having an English abstract). For QoL 40 articles were included, 85 for psychiatric comorbidities, and 9 for work/school absenteeism/presenteeism mostly consisting of cross-sectional and observational cohort studies. QoL impairment was detected in over 75% of patients and up to one-third reported extremely severe QoL impairments. Specific QoL dimensions with the greatest impact were embarrassment, social functioning, as well as shopping and/or housework. Cross-sectional studies assessing the psychological burden of adult patients with AA found that the presence of signs of anxiety and/or depression ranged from 30% to 68% and affected all age groups. Rates of work absenteeism and unemployment were significantly higher in AA patients compared to healthy controls. Up to 62% reported making major life decisions including relationships, education and career based on their AA. Additionally, the extensive camouflage techniques and time lost from work led to a strong financial burden for patients and the numerous physician visits added to the healthcare costs. The overall impact of AA stretches much further than simply being an aesthetic concern and can negatively impact every part of an individual's life. An individualized approach and effective treatments will help reduce the psychosocial consequences and distress and return patients to their normal state of health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".