LIVING WITH SYSTEMIC LUPUS ERYTHEMATOSUS IN 2024: LATIN AMERICAN EXPERIENCE BASED ON A PATIENT SURVEY
Bibliographic record
Abstract
PV169 / #263 Poster Topic: AS19 - Patient-Reported Outcome Measures Background/Purpose Systemic lupus erythematosus (SLE) is a systemic autoimmune disease that significantly impacts patients’ quality of life. In 2020, a survey was conducted by Lupus Europe to assess the burden of SLE among European patients.[1] The reality of Latin America (LA) is highly diverse in terms of healthcare access and treatment availability, making it essential to describe these experiences from the patients’ perspective. This study aimed to evaluate the burden of SLE from the perspective of LA patients in 2024. Methods In May 2024, as part of the international SLE awareness day, the Grupo Latinoamericano de Estudio del Lupus (GLADEL) disseminated an anonymous, bilingual online survey (in Spanish and Portuguese) to patients diagnosed with SLE through their physicians and various patient associations across LA countries. Results Data from 2,139 SLE respondents (95.9% female, median age: 38.0 years [IQR: 31.0–46.0], 25.5% Caucasian, 34.7% Mestizo, 20% Afrolatinoamerican, 10% indigenous and 6.8% other) from 15 LA countries were analyzed. The most commonly affected organs were the joints (68.1%), the skin (47.3%), and the kidney (35.6%). In 52.5% of the cases, a previous diagnosis other than SLE was reported. Regarding educational level, 40% had completed high school. At the time of the survey, 40% were employed, while 20% had stopped working due to lupus. Daily life activities were negatively impacted by lupus for 40% of respondents, with joint pain (17.5%) and fatigue (35%) being the most disruptive symptoms. Additionally, 35.6% of patients used antimalarials, 25% were on steroids (mean dose: 5 mg/day), 27.8% used immunosuppressants, and 5.4% were receiving biologic drugs. Notably, 35% of respondents agreed that they had access to specialized care and treatments appropriate for their condition. Conclusions Understanding patient perspectives is crucial for evaluating the impact of SLE and addressing challenges related to healthcare access and treatment. Incorporating patient feedback into regional healthcare policies should be prioritized. References: [1.] Cornet A. Lupus Sci Med 2021;8:e000469. Acknowledgments: LUPUS EUROPE for the support in adapting the survey to LA, to the GLADEL researchers and all patients who voluntarily participated in this survey and to the patient associations that made the survey available to patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".