1303 The new EULAR/ACR 2019 SLE classification criteria: a predictor of long-term outcomes
Bibliographic record
Abstract
<h3>Background</h3> We recently demonstrated that a EULAR/ACR classification Criteria score ≥ 20 predicts a higher disease activity throughout the first 5 years after diagnosis. Given that disease activity is associated with damage accrual and mortality, we aimed to determine the ability of a EULAR/ACR score ≥20 to predict these long-term outcomes. <h3>Methods</h3> Inception SLE patients recruited in the first 12 months after diagnosis were included. For each patient a EULAR/ACR score was calculated based on the baseline clinical and laboratory information. The baseline information was obtained from the first 2 visits. Patients were divided into 2 groups depending on their EULAR/ACR score <20 or ≥20. In order to determine the ability of a EULAR/ACR ≥20 to predict damage accrual and mortality the following outcomes were assessed: Time to first damage accrued: Defined as the first increase in SLICC/ACR Damage Index from 0 to ≥ 1 within the first 10 years after SLE diagnosis, with death as a competing risk. 57 patients with damage at entry were excluded Time to first increase in damage: Defined as any increase in the SLICC/ACR Damage Index within the first 10 years after SLE diagnosis, with death as a competing risk. Mean SDI score at the 10th year of follow–up. Time to death within the first 10 years after SLE diagnosis Multivariable Cox Proportional regression was performed to calculate the risk and possible confounders. <h3>Results</h3> A total of 867 inception patients were included. Table 1 shows baseline clinical characteristics of the cohort. The proportion of patients who accrued damage within the first 10 years and the mean SDI at 10 years were significantly higher in the group of ≥ 20. When looking at the specific domains in SDI, the group with a score ≥ 20 at 10 years of follow-up had significantly more renal damage and a higher percentage of diabetes (table 2). On multivariable regression analysis, after adjusting for age and ethnicity, a score ≥ 20 continued to significantly predict damage accrual, HR 1.28 (1.04-1.57), p=0.02. When we excluded patients who had damage at enrollment the results were similar (table 3). Sixty-eight (7.8%) of patients died within the first 10 years of follow-up, the percent of deaths was higher in the group with a score ≥ 20, (table 2). Individuals in the ≥ 20 group had twice the probability of dying compared to patients with the lower score, the hazard ratios with significant p values confirmed this finding (table 3). <h3>Conclusion</h3> A EULAR/ACR score ≥20 is an indicator of damage accrual and mortality in SLE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".