Effect of sustained low efficient dialysis <i>versus</i> continuous renal replacement therapy on renal recovery after acute kidney injury in the intensive care unit: A systematic review and meta‐analysis
Bibliographic record
Abstract
Critically ill adults with acute kidney injury (AKI) experience considerable morbidity and mortality. Controversy remains regarding the optimal renal replacement intervention for these patients. Our systematic review aimed to determine the effect(s) of sustained low-efficiency dialysis (SLED) compared with continuous renal replacement (CRRT) therapy on relevant patient outcomes. A systematic search of Medline, Embase, CINAHL and the Cochrane Library was conducted. Identified citations were screened independently in duplicate for relevance, and the methodological quality of included studies was evaluated. Data were extracted on study, patient and intervention characteristics and relevant clinical outcomes. Results were pooled using inverse variance fixed and random effects meta-analysis. A total of 1564 patients from 18 studies were included. Meta-analysis results indicated no statistically significant difference in our primary outcome, overall proportion of renal recovery (risk ratio (RR) 0.87, 95% confidence interval (CI) 0.63-1.20, I2 = 66%). No significant difference was observed for the secondary outcome of time to renal recovery (mean difference 1.33, 95% CI 0.23-2.88, I2 = 0%). Statistically, SLED was marginally favoured over CRRT for the secondary outcome of mortality (RR 1.21, 95% CI 1.02-1.43, I2 = 47%); however, this diminished when sensitivity analysis of only randomized controlled trials was conducted (RR 1.25, 95% CI 1.00-1.57, I2 = 0%). There appears to be no clear for advantage continuous renal replacement in the hemodynamically unstable patient. Currently, both modalities are safe and effective means of treating AKI in the critically ill adult.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".