Focal and Segmental Glomerulosclerosis
Bibliographic record
Abstract
Focal and segmental glomerulosclerosis (FSGS) is one of the most common primary glomerular diseases to terminate in ESRD. A complete remission (CR) confers an excellent long-term prognosis, but the quantitative benefits of partial remissions (PR) have not been defined. This study evaluated the rate of renal function decline (slope of creatinine clearance) and renal survival in nephrotic FSGS patients with CR, PR, or no remission. It also examined relapse rate from remission and its impact on outcome. Multivariate analysis included clinical and laboratory data at presentation and over follow-up, BP control, the agents used, and immunosuppressive therapy. The study cohort was 281 nephrotic FSGS patients who had a minimum of 12 mo of observation and were identified from the Toronto Glomerulonephritis Registry. Over a median follow-up of 65 mo, 55 experienced a CR, 117 had a PR, and 109 had no remission. A PR was independently predictive of slope and survival from renal failure by multivariate analysis (adjusted time-dependent hazard ratio, 0.48; 95% confidence interval, 0.24 to 0.96; P = 0.04). Immunosuppression with high-dose prednisone was associated with a higher rate of PR and CR. Relapse from PR was frequent (56%) and associated with a more rapid rate of renal function decline and worse renal survival compared with relapse-free partial remitters. Only female gender and the nadir of proteinuria during remission were associated with a sustained remission. A PR in proteinuria and its maintenance are important therapeutic targets in FSGS, with implications for both slowing progression rate and improving renal survival.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".