1301 Effect of attribution on external validation of the EULAR/ACR SLE classification criteria
Notice bibliographique
Résumé
<h3>Background</h3> With their new structure of ever positive anti-nuclear antibodies (ANA) as an obligatory entry criterion and weighted specific criteria with a cut-off of ≥ 10, the European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) 2019 classification criteria for systemic lupus erythematosus (SLE) has a sensitivity of 96.1% and a specificity of 93.4% in the validation cohort.<sup>1,2</sup> An analysis of the performance of the individual criteria items found that the specificity of joint involvement was 90.9%, but would drop to 57.6% if the attribution rule was not applied.<sup>3</sup> The attribution rule states that only those items should be counted towards classification that have no alternative explanation more likely than SLE. The new criteria have been externally validated in a number of studies. From many of the external validation studies, it is not clear whether this attribution rule was followed <h3>Methods</h3> A literature search was performed for „lupus criteria‘. Titles and abstracts were screened for studies that (i) referred to the EULAR/ACR criteria (even if using different terms) and (ii) indicated sensitivity and/or specificity estimates. The association between criteria specificity and frequency of joint involvement in the non-SLE control group and association between ANA positivity and criteria sensitivity were evaluated. <h3>Results</h3> Operating characteristics of the SLE classification criteria have been evaluated in 19 studies. The external validation studies reported a sensitivity range of 84.8-97.6% and specificity range of (58.4-97.3%) (table 1). Specificity was evaluated in 14 studies. In 3 of the studies appropriate use of the attribution rule was apparent. One study was excluded for focusing on neuropsychiatric manifestations. For the remaining 10 populations, there was a significant negative correlation between specificity and joint disease in the non-SLE control population. (r=−0.73, p=0.016), as depicted in figure 1 (left panel). Sensitivity estimates are reported in 19 studies, and the percentage of ANA positive SLE patients was reported for 17 of these. There was a positive correlation between ANA positivity and criteria sensitivity (r=0.50, p=0.043). (figure 1, right panel) <h3>Conclusions</h3> Specificity of the EULAR/ACR criteria is dependent on the correct use of the attribution rule. Higher percentages of patients with joint involvement in the non-SLE control populations is associated with a lower EULAR/ACR criteria specificity. Since joint involvement is particularly vulnerable to not using attribution, this suggests that the lower specificity in some external validation studies in part is due to not fully applying the attribution rule. Sensitivity was high throughout the analyzed studies. It is therefore crucial to differentiate between classification and diagnosis and keep in mind that not fulfilling SLE classification criteria is no valid argument against diagnosing SLE in an individual patient. <h3>References</h3> Aringer M, Costenbader K, Daikh D, et al. <i>Ann Rheum Dis</i> 2019; 78: 1151-1159. Aringer M, Costenbader K, Daikh D, et al. <i>Arthritis Rheumatol</i> 2019; 71: 1400-1412. Aringer M, Brinks R, Dörner T, et al. <i>Ann Rheum Dis</i> 2021; 80: 775-781
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 | 0,001 |
| 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 ».