A comparison of damage accrual across different calendar periods in systemic lupus erythematosus patients
Bibliographic record
Abstract
Therapeutic approaches in systemic lupus erythematosus (SLE) have evolved over the last few decades, but their impact on prevention of organ damage is unknown. The objective of this study was to compare new cumulative damage in SLE patients across different calendar periods. Patients from a large SLE cohort were divided into two subcohorts; the first diagnosed and followed between 1978 and 1988 (cohort #1, n=100) and the second between 1989 and 1999 (cohort #2, n=51). Initial Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SLICC/ACR DI) scores, and changes in scores over the observation intervals, were compared for the two groups. Logistic regression estimated adjusted odds ratios (OR) comparing damage accrual between the two cohorts. Medication exposures were noted. Baseline characteristics were similar between the two groups. At first assessment, the adjusted OR for a SLICC/ACR DI score > or =1 was 1.79 (95% CI 0.82, 3.88) for cohort #1 versus cohort #2. At the end of the observation interval, the adjusted OR for a SLICC/ACR DI score > or =1 was 1.22 (0.58, 2.55) for cohort #1 versus cohort #2. The adjusted OR for accruing damage over the observation interval in cohort #1 versus cohort #2 was 0.94 (0.39, 2.44). Increased medication exposure was evident for cohort #2 compared to cohort #1. Despite increased therapeutic measures used for patients in more recent periods, our data do not establish a clear difference in damage accrual. This emphasizes the need for strategies to effectively treat lupus-specific manifestations, while minimizing side effects and comorbidities.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".