CLINICAL MANIFESTATIONS AT DIFFERENT FOLLOW-UP TIME POINTS IN AUTOANTIBODY-DEFINED SLE SUBGROUPS.
Bibliographic record
Abstract
PV179 / #524 Poster Topic: AS20 - Precision Medicine Background/Purpose The heterogeneity of SLE has impaired the advancement in diagnostic strategies, tailored treatments, and prognostic tools for this disease. Defining SLE subgroups based on autoantibody profiles can reduce such heterogeneity and reveal important differences.[1] Here, we studied a Norwegian inception cohort, with longitudinal follow-up at 2 years and 11 years (in median IQR[7-13]) after diagnosis. Methods We clustered 102 SLE patients, as previously,[1] based on 10 autoantibodies at diagnosis (Table 1, Figure 1A). The autoantibodies were measured using ELISA or immunoprecipitation, we assumed positivity if 1 of them was positive. Using logistic or linear regression, we tested associations between clusters and 13 clinical manifestations at diagnosis, 2-year, and last visit. We tested associations between the clusters and SLEDAI and some of its components: acute cutaneous lupus, cardiovascular disease, lung, or muscle-skeletal involvement for the last follow-up. Analyses were done in R v4.3.3. Table 1. Characteristics of the SLE cohort from Norway. Figure 1. Results Four clusters explained most variability based on the Silhouette index (Figure 1). The patients in subgroup 2 have a higher risk of photosensitivity at diagnosis (OR:12.5 95% CI:2.1-252.8) and 2 years after (OR:8.1 95% CI:1.7-76.8). In comparison, photosensitivity was less common in subgroup 3 compared to the rest of patients at 2 years (OR:0.3 95% CI:0.09-0.7) (Figure 2B). Acute cutaneous lupus was more frequent in subgroup 2 at the last visit (OR:3.7 95% CI:1.2-14.1), lung involvement was more frequent in subgroup 1 (OR:14.5 95% CI:11.4-543.2), although it did not reach significance (p-value=0.053). SLEDAI significantly differed among the subgroups at diagnosis being higher for subgroups 4 and 3 (respectively: OR:1.5 95% CI:1.3-1.8; OR:1.3 95% CI:1.1-1.5), conversely lower for subgroups 2 and 1 (respectively: OR:0.7 95% CI:0.6-0.9; OR:0.7 95% CI:0.6-0.8). At the last visit, SLEDAI was significantly lower for subgroup 3 (OR:0.7 95% CI:0.5-0.9). Figure 2. Conclusions Patients with SLE from Norway can be grouped into 4 based on their antibody profile at the time of the diagnosis. Regardless of the limited sample size, the observations indicate that antibody-defined subgroups are a tool to reduce SLE heterogeneity and improve perspective to study this disease. References: [1.] Diaz-Gallo LM. ACR Open Rheumatol 2022;4(1):27-39.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".