The paradox of eGFR trends and kidney failure incidence in patients without monogenic kidney disorders. Reply.
Bibliographic record
Abstract
In regard to the issue of CureGN participants with diabetes, the exclusion of individuals with diabetes occurred at the time of enrollment into the study, not at the time of biopsy.We used the presence of diabetes mellitus at the time of kidney biopsy as a covariate in our analyses, as diabetes is well described to influence kidney disease progression and outcomes (1).Only a small proportion of the CureGN cohort (18 individuals, 1%) had diabetes mellitus at the time of biopsy that had resolved at enrollment and were thus included in the analysis cohort.With regard to the apparently discordant eGFR slope and kidney failure risk in the CureGN cohort without monogenic kidney disorders, this is likely due to the impact of treatment and remission rates on eGFR and kidney failure.As demonstrated in multiple glomerular disorders, including IgA nephropathy and focal segmental glomerulosclerosis, remission is a major driver of clinical outcomes including kidney failure risk and eGFR decline (2-4).A prior study of the CureGN IgA nephropathy cohort demonstrated that there is an improvement in eGFR between biopsy (mean 70.6 mL/min/1.73m 2 ) and enrollment (mean 75.8 mL/min/1.73m 2 ) as well as (5) focal segmental glomerulosclerosis, membranous nephropathy, or IgA nephropathy (IgAN).Within the CureGN cohort without monogenic kidney disorders, 67% (1,144 individuals) achieved complete remission within the follow-up time.Within this group who achieved complete remission, 4% (45 individuals) reached kidney failure and overall these individuals experienced an eGFR increase of 0.82 mL/min/year.Within the group who did not achieve complete remission, 23% (127 individuals) reached kidney failure and overall these individuals experienced an eGFR decline of 2.68 mL/min/year (P value for difference in eGFR slope < 0.001; Figure 1).Thus there is a group of individuals who continue to have progressive kidney disease and develop kidney failure over time, mainly those who do not achieve complete remission, while the majority of the cohort experiences an improvement in their eGFR with a lower risk of kidney failure driving the average eGFR change upwards and a lower risk of kidney failure overall.These findings are well aligned with the literature and highlight the observation that individuals with monogenic kidney disorders are less likely to achieve complete remission.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".