Past Decline Versus Current eGFR and Subsequent ESRD Risk
Bibliographic record
Abstract
eGFR is a robust predictor of ESRD risk. However, the prognostic information gained from the past trajectory (slope) beyond that of the current eGFR is unclear. We examined 22 cohorts to determine the association of past slopes and current eGFR level with subsequent ESRD. We modeled hazard ratios as a spline function of slopes, adjusting for demographic variables, eGFR, and comorbidities. We used random effects meta-analyses to combine results across studies stratified by cohort type. We calculated the absolute risk of ESRD at 5 years after the last eGFR using the weighted average baseline risk. Overall, 1,080,223 participants experienced 5163 ESRD events during a mean follow-up of 2.0 years. In CKD cohorts, a slope of -6 versus 0 ml/min per 1.73 m(2) per year over the previous 3 years (a decline of 18 ml/min per 1.73 m(2) versus no decline) associated with an adjusted hazard ratio of ESRD of 2.28 (95% confidence interval, 1.88 to 2.76). In contrast, a current eGFR of 30 versus 50 ml/min per 1.73 m(2) (a difference of 20 ml/min per 1.73 m(2)) associated with an adjusted hazard ratio of 19.9 (95% confidence interval, 13.6 to 29.1). Past decline contributed more to the absolute risk of ESRD at lower than higher levels of current eGFR. In conclusion, during a follow-up of 2 years, current eGFR associates more strongly with future ESRD risk than the magnitude of past eGFR decline, but both contribute substantially to the risk of ESRD, especially at eGFR<30 ml/min per 1.73 m(2).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".