Predicting Relapse and Long-term Renal Outcomes in Patients with Relapsing and Remitting FSGS: A Retrospective Observational Cohort Study
Bibliographic record
Abstract
Primary focal and segmental glomerulosclerosis (FSGS) is the leading glomerulonephritis related cause of renal failure in developed countries. Change in twenty-four-hour urinary protein excretion is the key biomarker defining remission and relapse. Predictive models for relapses and progression to renal failure are lacking. We hypothesized that two time-to-event, multivariable, survival models employing clinical variables may be utilized to predict the risk of relapse from the time of remission and predict progression to renal failure from the time of first relapse.Risk prediction models were created to (1) estimate the absolute risk of relapse among primary FSGS patients at 12, 24 and 60 months after their first remission and, (2) among patients suffering a relapse, to estimate the absolute risk of renal failure at 60 months. The model assessing relapse had poor discrimination, but reasonable calibration at 24 and 60 months. The model assessing renal failure had good discrimination with reasonable calibration.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".