Measuring Disease Damage and Its Severity in Childhood‐Onset Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To describe the frequency and types of disease damage occurring with childhood-onset systemic lupus erythematosus (SLE) as measured by the 41-item Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI), and to assess the SDI's ability to reflect damage severity. METHODS: Information for the SDI was prospectively collected from 1,048 childhood-onset SLE patients. For a subset of 559 patients, physician-rated damage severity measured by visual analog scale (MD VAS damage) was also available. Frequency of SDI items and the association between SDI summary scores and MD VAS damage were estimated. Finally, an international consensus conference, using nominal group technique, considered the SDI's capture of childhood-onset SLE-associated damage and its severity. RESULTS: After a mean disease duration of 3.8 years, 44.2% of patients (463 of 1,048) already had an SDI summary score >0 (maximum 14). The most common SDI items scored were proteinuria, scarring alopecia, and cognitive impairment. Although there was a moderately strong association between SDI summary scores and MD VAS damage (Spearman's r = 0.49, P < 0.0001) in patients with damage (SDI summary score >0), mixed-effects analysis showed that only 4 SDI items, each occurring in <2% of patients overall, were significantly associated with MD VAS damage. There was consensus among childhood-onset SLE experts that the SDI in its current form is inadequate for estimating the severity of childhood-onset SLE-associated damage. CONCLUSION: Disease damage as measured by the SDI is common in childhood-onset SLE, even with relatively short disease durations. Given the shortcomings of the SDI, there is a need to develop new tools to estimate the impact of childhood-onset SLE-associated damage.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".