The 2020 International Alliance for the Control of Scabies Consensus Criteria for the Diagnosis of Scabies
Notice bibliographique
Résumé
BACKGROUND: Scabies is a common parasitic skin condition that causes considerable morbidity globally. Clinical and epidemiological research for scabies has been limited by a lack of standardization of diagnostic methods. OBJECTIVES: To develop consensus criteria for the diagnosis of common scabies that could be implemented in a variety of settings. METHODS: Consensus diagnostic criteria were developed through a Delphi study with international experts. Detailed recommendations were collected from the expert panel to define the criteria features and guide their implementation. These comments were then combined with a comprehensive review of the available literature and the opinion of an expanded group of international experts to develop detailed, evidence-based definitions and diagnostic methods. RESULTS: The 2020 International Alliance for the Control of Scabies (IACS) Consensus Criteria for the Diagnosis of Scabies include three levels of diagnostic certainty and eight subcategories. Confirmed scabies (level A) requires direct visualization of the mite or its products. Clinical scabies (level B) and suspected scabies (level C) rely on clinical assessment of signs and symptoms. Evidence-based, consensus methods for microscopy, visualization and clinical symptoms and signs were developed, along with a media library. CONCLUSIONS: The 2020 IACS Criteria represent a pragmatic yet robust set of diagnostic features and methods. The criteria may be implemented in a range of research, public health and clinical settings by selecting the appropriate diagnostic levels and subcategories. These criteria may provide greater consistency and standardization for scabies diagnosis. Validation studies, development of training materials and development of survey methods are now required. What is already known about this topic? The diagnosis of scabies is limited by the lack of accurate, objective tests. Microscopy of skin scrapings can confirm the diagnosis, but it is insensitive, invasive and often impractical. Diagnosis usually relies on clinical assessment, although visualization using dermoscopy is becoming increasingly common. These diagnostic methods have not been standardized, hampering the interpretation of findings from clinical research and epidemiological surveys, and the development of scabies control strategies. What does this study add? International consensus diagnostic criteria for common scabies were developed through a Delphi study with global experts. The 2020 International Alliance for the Control of Scabies (IACS) Criteria categorize diagnosis at three levels of diagnostic certainty (confirmed, clinical and suspected scabies) and eight subcategories, and can be adapted to a range of research and public health settings. Detailed definitions and figures are included to aid training and implementation. The 2020 IACS Criteria may facilitate the standardization of scabies diagnosis.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,135 | 0,183 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,006 |
| Bibliométrie | 0,016 | 0,007 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,010 | 0,011 |
| Intégrité de la recherche | 0,013 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,005 |
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 source (Gemma direct ou Codex distillé), 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 ».