Sex Differences Across Corticosteroid Response and Outcomes in IgA Nephropathy
Bibliographic record
Abstract
Introduction The impact of sex on treatment response and outcomes in IgA Nephropathy (IgAN) remains uncertain. This analysis of the TESTING trial aimed to evaluate 1) the effect of sex on corticosteroid response and 2) the association between sex and kidney outcomes in IgAN. Methods Participants with IgAN were randomised to 6-9-months of oral methylprednisolone or placebo. The primary outcome was a composite of 40% decline in estimated glomerular filtration rate (eGFR), kidney failure or death due to kidney disease. The interaction between sex and treatment on the risk of the primary outcome, relative change in proteinuria and total eGFR slope was tested. Cox models were used to estimate the risk of the primary outcome in males vs. females. Sex-differences in total eGFR slope were also estimated. Results Overall, there were 305 (61%) males and 198 (39%) females. The mean follow-up period was 4.2 years. Compared to placebo, methylprednisolone lowered the risk of the primary outcome in both males (HR 0.51, 95% CI 0.35-0.74) and females (HR 0.64, 95% CI 0.38-1.09) ( P -interaction=0.47), and reduced proteinuria from baseline at 12 months and slowed the rate of total eGFR decline, with no sex-differences ( P -interaction=0.28 and 0.81, respectively). Males had a greater risk of the primary outcome compared to females (HR 1.44, 95% CI 1.05-1.97, P =0.03). Total eGFR rate of decline over two years was greater in males compared to females by 3.13mL/min/1.73m 2 /year (95% CI 0.92-5.34, P =0.006). Conclusion Methylprednisolone improves kidney outcomes in IgAN, regardless of sex, however males experience poorer kidney outcomes compared to females.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".