Evaluation of Short-Term Changes in Serum Creatinine Level as a Meaningful End Point in Randomized Clinical Trials
Bibliographic record
Abstract
Observational studies have shown that acute change in kidney function (specifically, AKI) is a strong risk factor for poor outcomes. Thus, the outcome of acute change in serum creatinine level, regardless of underlying biology or etiology, is frequently used in clinical trials as both efficacy and safety end points. We performed a meta-analysis of clinical trials to quantify the relationship between positive or negative short-term effects of interventions on change in serum creatinine level and more meaningful clinical outcomes. After a thorough literature search, we included 14 randomized trials of interventions that altered risk for an acute increase in serum creatinine level and had reported between-group differences in CKD and/or mortality rate ≥3 months after randomization. Seven trials assessed interventions that, compared with placebo, increased risk of acute elevation in serum creatinine level (pooled relative risk, 1.52; 95% confidence interval, 1.22 to 1.89), and seven trials assessed interventions that, compared with placebo, reduced risk of acute elevation in serum creatinine level (pooled relative risk, 0.57; 95% confidence interval, 0.44 to 0.74). However, pooled risks for CKD and mortality associated with interventions did not differ from those with placebo in either group. In conclusion, several interventions that affect risk of acute, mild to moderate, often temporary elevation in serum creatinine level in placebo-controlled randomized trials showed no appreciable effect on CKD or mortality months later, raising questions about the value of using small to moderate changes in serum creatinine level as end points in clinical trials.
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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 | 0.346 | 0.411 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.035 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".