The Alopecia Areata Severity and Morbidity Index (ASAMI) Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».