A Pooled Analysis of Sex Differences in Lupus Nephritis
Bibliographic record
Abstract
Introduction: Lupus nephritis (LN) has been reported to be more severe and have worse outcomes in males than females. To determine if sex differences exist in disease severity at diagnosis or relapse, first treatment choice, response to initial treatment, and key kidney and patient outcomes, we performed a pooled analysis of data from 4 randomized controlled trials (RCTs) and 7 cohort studies of LN. Methods: The study population included an international representation of 831 females and 122 males with LN with a median follow-up of 41.4 months. Baseline characteristics and outcomes (complete renal response [CRR], doubling of creatinine (Cr), dialysis, death, LN relapse, or composite outcomes) were compared between sexes using the entire data set. Cohort data were used to analyze treatment choice. Mixed model regression with studies as the random effects, controlled for age and race, was used to assess the effect size of sex on key clinical variables. Logistic regressions controlling for treatments and relevant baseline characteristics were used to evaluate outcomes. Results: We found no significant sex differences in age, race, systemic lupus erythematosus (SLE) or LN duration, C3, anti-double-stranded DNA, LN class distribution, or SLE Disease Activity Index (SLEDAI), although males had lower baseline estimated glomerular filtration rate (eGFR). Therapy choice, CRR, kidney, or patient outcomes were similar between sexes. Limitations to our study include the absence of detailed description of kidney histopathology, and incomplete outcomes data. Conclusion: Apart from lower baseline eGFR, males and females with LN exhibit similar disease severity, treatment responses, kidney outcomes, and relapse-free survival in this large international analysis.
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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 | 0.041 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.028 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".