Determining the optimal time for liberation from renal replacement therapy in critically ill patients: protocol for a systematic review and meta-analysis (DOnE RRT)
Bibliographic record
Abstract
INTRODUCTION: Renal replacement therapy (RRT) is a complex and expensive form of life-sustaining therapy, reserved for our most acutely ill patients. While a number of randomised trials have evaluated the optimal timing to start RRT among critically ill patients in the intensive care unit (ICU), there has been a paucity of trials providing guidance on when and under what circumstances to ideally liberate a patient from RRT. We are conducting a systematic review and meta-analysis to identify clinical and biochemical markers that predict kidney recovery and successful liberation from acute RRT among critically ill patients with acute kidney injury. METHODS AND ANALYSIS: Our comprehensive search strategy was developed in consultation with a research librarian and independently peer-reviewed by a second librarian. We will search electronic databases: Ovid Medline, Ovid Embase and Wiley Cochrane Library. Selected grey literature sources will also be searched. Our search strategies will focus on concepts related to RRT (ie, intermittent haemodialysis, slow low-efficiency dialysis, continuous renal replacement therapy), intensive care (ie, involving any ICU setting) and discontinuation of therapy (ie, either clinical, physiological and biochemical parameters of weaning acute RRT) from 1990 to October 10, 2017. Citation screening, selection, quality assessment and data abstraction will be performed in duplicate. Studies will, where possible, be pooled in statistical meta-analysis. When deemed sufficiently clinically homogenous, and we have four or more studies reporting, sensitivities and specificities will be pooled simultaneously using a hierarchical summary receiver operator characteristic curve and bivariate analysis. ETHICS AND DISSEMINATION: Our systematic review will synthesise the literature on clinical and biochemical markers that predict liberation from RRT. Research ethics approval is not required. TRIAL REGISTRATION NUMBER: CRD42018074615.
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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 | 0.065 | 0.112 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.026 | 0.035 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.005 |
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".