AB0578 LATIN-AMERICAN SYSTEMIC LUPUS ERYTHEMATOSUS CLUSTERS
Bibliographic record
Abstract
Background Systemic lupus erythematosus (SLE) is heterogeneous autoimmune disease. Identification of patient clusters may be useful for the management of the disease. Objectives To describe different SLE clusters according to sociodemographic, clinical and serological variables. Methods GLADEL 2.0 is an ongoing Latin-American observational cohort initiated in 2019. Variables chosen at cohort entry to stratify patients and construct clusters were selected from sociodemographic and cumulative clinical and serological variables. Hierarchical cluster analyses were performed by the Ward method on a distance matrix using the Gower’s method. Results A total of 560 SLE patients were included in this analysis. Three clusters were identified. Cluster 1 (n=269) was characterized by more cutaneous, articular, renal and serosal involvement; serological manifestation was positive anti-dsDNA. Cluster 2 (n=194) was represented by patients who rarely had renal involvement and the most frequent clinical manifestations were cutaneous and hematological; the most frequent serological manifestations were the presence of antiphospholipid antibodies (aPLs). Cluster 3 (n=97) was characterized by a lower frequency of clinical and serological involvements, with the exception of neurological domain. Clusters 1 and 2 share hematologic manifestations and hypocomplementemia (Table 1). Conclusion In this cohort, three patient clusters were identified. Cluster 1 patients were characterized by renal, articular, cutaneous and serositis involvement, anti-dsDNA antibodies and hypocomplementemia, Cluster 2 patients were characterized by hematologic, cutaneous involvement, aPLs and hypocomplementemia. Cluster 3 patients presented fewer serological findings but a higher frequency of neurological involvement. Follow up of these patients will allow for elucidation of relationship of these clusters with SLE outcomes. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests Rosana Quintana: None declared, Romina Nieto: None declared, Jose A. Gómez-Puerta: None declared, Guillermina B. Harvey: None declared, Marina Scolnik: None declared, Nidia Meras: None declared, Cintia Otaduy: None declared, Elisa Novatti: None declared, Valeria Arturi: None declared, Maria Emilia Sattler: None declared, Guillermo Pons-Estel: None declared, Luciana Gonzalez Lucero: None declared, Wilfredo Patiño Grageda: None declared, Nicolas Perez: None declared, Cecilia Pisoni: None declared, Ana Carolina de Oliveira e Silva Montandon: None declared, Odirlei Monticielo: None declared, Angela Duarte: None declared, Francinne Machado Ribeiro: None declared, Emily Figueiredo Neves Yuki: None declared, Edgard Reis Neto: None declared, Iris Guerra Herrera: None declared, Milena Mimica: None declared, Gustavo Aroca Martínez: None declared, Gabriel J. Tobón: None declared, GERARDO QUINTANA LOPEZ: None declared, Andrés Cadena Bonfanti: None declared, Mario Javier MORENO ALVAREZ: None declared, Miguel A Saavedra: None declared, Margarita Portela: None declared, Hilda Fragoso loyo: None declared, Luis Humberto Silveira Torre: None declared, JUAN IGNACIO GARCIA VALLADARES: None declared, Carlos Abud-Mendoza: None declared, Jorge Antonio Esquivel Valerio: None declared, Maria Martínez: None declared, Margarita Duarte: None declared, CLAUDIA MORA: None declared, Manuel F. Ugarte-Gil: None declared, Ernesto Zavala: None declared, Roberto Muñoz Louis: None declared, RICARDO ROBAINA: None declared, Vicente Juarez: None declared, Gonzalo Silveira: None declared, Eduardo Borba: None declared, Luis Catoggio: None declared, Graciela S Alarcon: None declared, Federico Zazzetti Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Horsham, PA, USA, Ashley Orillion Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, PA, USA, Urbano Sbarigia Employee of: Janssen Pharmaceutica NV, Beerse, BE, Bernardo Pons-Estel: None declared.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".