Filtration Markers May Have Prognostic Value Independent of Glomerular Filtration Rate
Bibliographic record
Abstract
Serum levels of creatinine, cystatin C, or β trace protein allow estimation of GFR, but whether these markers contribute additional prognostic information beyond that reflected in GFR is unknown. Here, we analyzed data from the Modification of Diet in Renal Disease study, which provided baseline levels of these markers for 816 participants with a median follow-up of 16.6 years. We examined associations between the reciprocals of these filtration markers and (125)I iothalamate GFR, expressed per SD, with kidney failure and mortality. In univariate analysis, lower GFR and higher levels of each filtration marker associated with a higher risk for all outcomes. After adjustment for GFR in a Cox proportional hazards model, higher creatinine associated with a higher risk for kidney failure but a lower risk for all-cause mortality. Higher cystatin C and β trace protein associated with a higher risk for both kidney failure and all-cause mortality. In models including either cystatin C or β trace protein, the association of GFR with all-cause mortality was no longer significant after the addition of the filtration marker, suggesting the possibility of multicollinearity. In summary, after adjustment for GFR, levels of creatinine, cystatin C, and β trace protein, each remained directly associated with kidney failure but differed with respect to their associations with mortality. These differences may be a result of non-GFR-related associations of filtration markers, residual confounding by GFR, or collinearity between the filtration markers and GFR. β trace protein and cystatin C seem to provide more consistent prognostic information than creatinine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".