Predictors of damage accrual by organ domain in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: Organ damage in SLE is assessed using the SLICC/ACR Damage Index (SDI), which quantifies damage in 12 organ systems but generally reports a total score. We examined whether the risk factors for overall organ damage accrual captured those associated with domain-specific damage accrual in patients with SLE. METHODS: Data from a 13-country longitudinal SLE cohort were collected between 2013 and 2020 using standard templates. SDI was used to assess overall and domain-specific organ damage. A series of multi-failure, multivariate models were performed to examine the risk factors associated with overall and domain-specific damage accrual. RESULTS: A total of 3449 patients with a median of 2.8 [interquartile range (IQR): 1.1, 5.6] years of follow-up were studied. Twenty-one percent (n = 717) patients accrued damage in at least one domain during the study period (musculoskeletal, 6%; renal and ocular, 5% each, neuropsychiatric, 2.5%; <2%, other domains). Risk factors for damage accrual differed among domains. Older age, cumulative glucocorticoid dose, existing skin damage, and diabetes were strong predictors in multiple but discrete domains. Asian ethnicity conferred greater risk of ocular, musculoskeletal, and diabetic damage accrual but was protective against peripheral vascular damage accrual. Smoking was a significant risk factor for peripheral vascular and skin damage accrual, while male sex was associated with the malignancy domain. CONCLUSION: Risk factors for individual organ system damage accrual were highly varied in patients with SLE. Not all factors associated with domain-specific damage accrual were captured by the risk factors analysed for overall organ damage accrual. TRIAL REGISTRATION: ClinicalTrials.gov, http://clinicaltrials.gov, NCT03138941.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".