Practical and Relevant Guidelines for the Management of Psoriasis: An Inference-Based Methodology
Notice bibliographique
Résumé
Psoriasis (Pso) is a common, immune-mediated, chronic-relapsing, inflammatory skin disease. While a great deal is known about Pso and its treatment, there remain several treatment scenarios unaddressed by clinical studies. To be effective, treatment for Pso must alter the activity of one or more immunological pathways important in the pathogenesis of the disease. While the benefit of blocking these pathways may be apparent, there remain uncertainties regarding safety, such as infections, malignancies, and the potential for off-target effects. Existing guidelines and treatment recommendations rely primarily on clinical trial or observational data, none of which adequately address specific clinical challenges. This document describes a methodological framework for generating practical and clinically relevant guidance for situations where direct evidence is rare or absent. Guidelines implementing this framework are currently ongoing. We develop a knowledge synthesis approach to guideline development, utilizing clinical trial data where available, and a formalized inferential decision-making process that considers indirect data coupled with structured expert opinion and analysis. This approach is best suited for situations where direct, high-level evidence is lacking. Support for each resultant recommendation is expressed as a quantified assessment of confidence. The topics to be addressed by this set of guidelines are ranked by clinicians and patients as areas of concern, with an emphasis on topics where high-level evidence may have limited availability. Through this novel approach, we will derive practical, informative recommendations using the best evidence available in combination with structured expert opinion to guide best practices in complex, real-world settings. Clinical guidelines aim to assist doctors in managing their patients’ medical conditions. A limitation of current guidelines is that they are frequently based on randomized clinical research trials—often considered the gold standard in medical research. Clinical trials are designed to estimate the safety and effectiveness of treatment. Outside of clinical trials, doctors encounter a range of patient cases excluded from clinical trials. Our group aims to create guidelines for those clinical scenarios not adequately addressed by clinical trials. Examples include patients excluded from clinical trials, the elderly, patients with human immunodeficiency virus (HIV), and pregnant or breastfeeding women. When clinical trial data is limited, doctors must make decisions nonetheless. In certain clinical situations they are left to their own resources to consult with experts, review the data, and make inferences based on the limited data available. Instead of concluding that there is no data, the topic of interest can be broken down into components that are answerable by different types of research studies. This inference-based approach uses expert opinion and indirect evidence to support an inference-based position on topics where direct clinical data is sparse or insufficient to answer the question. This approach can be used as a complement to clinical trial data informing disease management guidelines.
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 | 0,001 | 0,002 |
| 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 ».