Predictors of successful discontinuation from renal replacement therapy during AKI: A meta‐analysis
Bibliographic record
Abstract
The predictors of weaning time of renal replacement therapy (RRT) remain controversial for special patients suffering from acute kidney injury (AKI). The present work aims to perform a meta-analysis to evaluate proper predictors of RRT weaning in AKI patients. We systematically searched EMBASE, PubMed, and Cochrane Central Register of Controlled trials for literatures between 1984 and June 2019. Studies evaluating predictors of weaning success of RRT in patients of AKI were included. Random-effects model or fixed-effects model meta-analyses were performed to compute a standard mean difference (SMD). Newcastle-Ottawa Scale was employed to assess the risk of bias. We included 10 observational trials including 1453 patients. Twelve predictors including urine output, serum creatinine, serum urea, mean arterial pressure, central venous pressure, lactate, serum potassium, serum bicarbonate, pH value, SOFA score, urinary urea, and urinary creatinine were identified, showing urine output (p = 0.0000), serum creatinine (p = 0.008), serum potassium (p = 0.02), serum bicarbonate (p = 0.01), pH value (p = 0.03), urinary urea (p = 0.002), and urinary creatinine (p = 0.02) were significantly associated with weaning success. With the limited evidence, we speculate that urine output, serum creatinine, serum potassium, serum bicarbonate, pH value, urinary urea, and urinary creatinine might be associated with successful weaning.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.048 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".