PAIN IN CUTANEOUS LUPUS ERYTHEMATOSUS CATEGORIES & SUBTYPES
Notice bibliographique
Résumé
PV060 / #81 Poster Topic: AS07 - Cutaneous Lupus Background/Purpose Cutaneous lupus erythematosus (CLE) is a chronic inflammatory autoimmune disease encompassing a broad range of dermatologic manifestations. CLE findings may be divided into LE-specific and LE-nonspecific skin disease, whereby LE-specific lesions show histopathologically distinct findings. Based on clinical characteristics, CLE can be categorized into 3 major categories of LE-specific skin disease and further into subtypes within each category (Table 1). Pain in CLE is an area that is understudied, with only 1 previous study showing that patients with both specific and nonspecific CLE lesions had higher pain levels than those with only 1 lesion type.[1] There have been no studies examining differences in pain across the categories and subtypes of CLE. In this study, we set out to delineate differences in pain across the various categories and subtypes of CLE as well as to examine how pain changes over time in order to better guide clinicians in evaluating for pain, which may negatively impact patient quality of life. Table 1. Comparison of Pain Scores Across CLE Categories/Subtypes Methods Subjects were selected from a longitudinal database of CLE patients seen at the outpatient autoimmune skin disease clinic of the Hospital of the University of Pennsylvania (HUP). Those included were patients with a diagnosis of CLE with moderate-to-severe disease as measured with the CLE Disease Area and Severity Index (CLASI), a validated clinical tool used to quantify disease activity and damage in CLE. Patients were classified by category into acute CLE (ACLE), subacute CLE (SCLE), and chronic CLE (CCLE). Within each category, patients were further subtyped (Table 1). Pain was quantified using a 10-cm visual acuity scale (VAS) for pain, with 0-cm as none and 10-cm as most severe. The Kruskal-Wallis test was used to compare median pain across all CLE categories and subtypes at their initial clinic visit. To examine change in pain over time, we used a linear mixed model (LMM) approach to look at pain across all visits within year 1 of initial presentation while controlling for disease duration. For the LMM analysis, only patients with a VAS pain score greater than 3 at their initial visit were included in order to capture patients who were experiencing at least mild pain. Results A total of 528 patients were included in the study. Across patients, 31% has a VAS pain score above 4, signaling moderate to severe pain. Generalized ACLE (ACLE-G) and chilblain had the highest median pain scores at visit 1, though this was not statistically significant (p>.05) (Table 1). Across all categories and subtypes, significant change in pain over time was only seen in ACLE (23 patients), and CCLE (121 patients) (p<.05) (Figure 1) with pain scores increasing in the year following visit 1. Figure 1: Linear mixed model analysis of change in pain over time across CLE categories and subtypes. ACLE = acute cutaneous lupus erythematous. CCLE = chronic cutaneous lupus erythematous. Median pain score represents pain visual analog scale (VAS) score across all visits. Conclusions Results from our study show that pain was not significantly different between CLE categories and subtypes at their initial visit. Significant change in pain across year 1 was observed in ACLE and CCLE with pain scores increasing. At initial presentation, almost one-third of patients had moderate to severe pain with a VAS pain score greater than 4. As pain may have a negative impact on quality of life, it should be carefully considered during not only initial clinical evaluation but subsequent visits. References: [1.] Méndez-Flores S. Clin Exp Rheumatol 2013;31(6):940-2.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,002 |
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 ».