Continuous versus intermittent renal replacement therapy for critically ill patients with acute kidney injury: A meta-analysis*
Bibliographic record
Abstract
OBJECTIVE: To appraise the literature on the effect of initial renal replacement therapy (RRT) modality on clinical outcomes. DESIGN: Systematic review and meta-analysis. SETTING: Academic medical center. PATIENTS AND PARTICIPANTS: Adult critically ill patients with acute kidney injury. INTERVENTIONS: Continuous vs. intermittent RRT. MEASUREMENTS AND RESULTS: MEDLINE, EMBASE, Cochrane Controlled Clinical Trials Register, and other sources were searched. We identified nine unique randomized trials (n = 1,403). No trial satisfied all quality indicators and several had limitations related to selection bias, randomization, imbalances in patient characteristics, and high treatment crossover. No trial standardized the timing, criteria, for initiation or dose of RRT. There was no statistical evidence that initial modality influenced mortality (odds ratio, 0.99; 95% confidence interval, 0.78-1.26, p = .93; I2 = 11%; nine trials, n = 1,403) or recovery to RRT independence (odds ratio, 0.76; 95% confidence interval, 0.28-2.07, p = .59; I2 = 0%; four trials, n = 306). There was suggestion that continuous RRT had fewer episodes of hemodynamic instability and better control of fluid balance. CONCLUSIONS: We identified numerous issues related to study design, conduct, and quality that dispute the validity and question any inferences that can be drawn from these trials. In the context of these limitations, the initial RRT modality did not seem to affect mortality or recovery to RRT independence. There is urgent need for additional high-quality and suitably powered trials to adequately address this issue.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".