Gender and ethnic origin have no effect on long-term outcome of childhood-onset systemic lupus erythematosus.
Bibliographic record
Abstract
Abstract \nOBJECTIVE: \n \nTo investigate the associations of gender and ethnic origin with longterm outcome in childhood-onset systemic lupus erythematosus (SLE). \nMETHODS: \n \nThe study cohort consisted of 51 patients (13 males and 38 females) with childhood-onset SLE followed for > or = 5 years at the British Columbia Children's Hospital in Vancouver. Fifteen patients were Caucasian, 14 Chinese, 9 East Indian, and 13 patients were of other ethnic backgrounds: none was African-American or Hispanic. Outcome measures assessed retrospectively included Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index score (SDI), SLE-related death, need for dialysis or renal transplantation, and use of intensive immunosuppressive therapy. A SDI > or = 2 was assigned as poor outcome. \nRESULTS: \n \nThe median age at diagnosis was 10.8 years and the median duration of followup was 7.2 years. Five-year survival was 100%; 10-year survival was 85.7% (12/14 patients). The median SDI score at last followup was 2.0 (range 0-9); 2.0 for male, 1.5 for female; 2.0 for Caucasian and 2.03 for non-Caucasian patients. Twenty-six out of 51 patients (51%) had poor outcome (SDI score > 2). Three female patients required dialysis: 2 had subsequent renal transplants. Thirty patients received intensive immunosuppressive therapy. The SDI scores, mortality, and need for intensive immunosuppressive therapy were not influenced by either gender or ethnic origin. \nCONCLUSION: \n \nThe median SDI score was high for this cohort with childhood-onset SLE. In contrast to other published data, no association of male gender and/or non-Caucasian ethnicity with poor outcome was found in our study cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".