Delivery of Renal Replacement Therapy in Acute Kidney Injury
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The prescription and delivery of renal replacement therapy for acute kidney injury is subject to a wide variation and is conditioned by a multiplicity of factors. A variety of renal replacement therapy modalities are now available to treat acute kidney injury; however, there are no standards for the dosage, choice of modality, and intensity and duration of these therapies. Although several observational and interventional studies have addressed these topics, there are no consensus recommendations in this field. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The available literature on this topic and draft consensus recommendations for research studies in this area were developed using a modified Delphi approach and an international multidisciplinary network. RESULTS: The following questions were most important: What is the "dosage" of renal replacement therapy delivered to patients with stage 3 acute kidney injury? What is the optimal "dosage" of renal replacement therapy to maximize patient and renal survival? Is there a minimal "dosage" of renal replacement therapy required in patients with single-organ failure? Does modality of renal replacement therapy selected have an effect on patient and/or renal survival? In cases of continuous renal replacement therapy, does citrate anticoagulation confer a benefit? CONCLUSIONS: This report summarizes the available evidence and elaborates on the key questions and the methods that should be used so that the goal of standardizing the care of patients with acute kidney injury and improving outcomes can be achieved.
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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 | 0.035 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".