Burden of Disease in Alopecia Areata: A Canadian Online Survey of Patients and Caregivers (Preprint)
Bibliographic record
Abstract
BACKGROUND Alopecia areata (AA) is associated with negative impacts on quality of life (QoL). Data is lacking on this impact for Canadian patients and their caregivers. OBJECTIVE To investigate the burden of AA on Canadian patients and their caregivers. METHODS Four online surveys were created for patients 5-11 years old, 12-17 years old, ≥18 years old, and for caregivers of children (<18 years old) with AA. These were disseminated through the Canadian Alopecia Areata Foundation website and to dermatologists across Canada. RESULTS One-hundred and fifteen adult patients (97% female), 14 pediatric patients (93% female) and 15 caregivers completed the surveys online. The majority (95%) of patients felt uncomfortable or self-conscious about their appearance. Camouflaging hair loss with hats, scarves, and hair pieces was a common practice for 78.6% of pediatric and 74% of adult patients. Avoidance of social situations was reported by 61.5% of pediatric and 68.2% of adult patients. Constant worry about losing achieved hair growth was a concern for 61.5% of pediatric and 68.2% of adult patients. On a scale of 1-5, the mean score of caregivers’ own feelings of sadness or depression about their child’s AA was 4.0 (SD 0.9), and their feelings of guilt or helplessness was 4.2 (SD 1.2). The impact on QoL was moderate for both children and adults. Based on the Adjustment Disorder New Module, 62% of patients were at high risk of an adjustment disorder. Abnormal anxiety scores were recorded in 35% of patients compared to abnormal depression scores in 17%. CONCLUSIONS This study confirmed significant burden of AA on Canadian patients’ and caregivers’ QoL.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".