Acute cardiorenal syndrome in acute heart failure: focus on renal replacement therapy
Bibliographic record
Abstract
Almost half of hospitalised patients with acute heart failure develop acute cardiorenal syndrome. Treatment consists of optimisation of fluid status and haemodynamics, targeted therapy for the underlying cardiac disease, optimisation of heart failure treatment and preventive measures such as avoidance of nephrotoxic agents. Renal replacement therapy may be temporarily needed to support kidney function, mostly in case of diuretic resistant fluid overload or severe metabolic derangement. The best timing to initiate renal replacement therapy and the best modality in acute heart failure are still under debate. Several modalities are available such as intermittent and continuous renal replacement therapy as well as hybrid techniques, based on two main principles: haemofiltration and haemodialysis. Although continuous techniques have been associated with less haemodynamic instability and a greater chance of renal recovery, cohort data are conflicting and randomised controlled trials have not shown a difference in recovery or mortality. In the presence of diuretic resistance, isolated ultrafiltration with individualisation of ultrafiltration rates is a valid option for decongestion in acute heart failure patients. Practical tools to optimise the use of renal replacement therapy in acute heart failure-related acute cardiorenal syndrome were discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".