Prevention and treatment of sepsis-induced acute kidney injury: an update
Bibliographic record
Abstract
Sepsis-induced acute kidney injury (SAKI) remains an important challenge in critical care medicine. We reviewed current available evidence on prevention and treatment of SAKI with focus on some recent advances and developments. Prevention of SAKI starts with early and ample fluid resuscitation preferentially with crystalloid solutions. Balanced crystalloids have no proven superior benefit. Renal function can be evaluated by measuring lactate clearance rate, renal Doppler, or central venous oxygenation monitoring. Assuring sufficiently high central venous oxygenation most optimally prevents SAKI, especially in the post-operative setting, whereas lactate clearance better assesses mortality risk when SAKI is present. Although the adverse effects of an excessive "kidney afterload" are increasingly recognized, there is actually no consensus regarding an optimal central venous pressure. Noradrenaline is the vasopressor of choice for preventing SAKI. Intra-abdominal hypertension, a potent trigger of AKI in post-operative and trauma patients, should not be neglected in sepsis. Early renal replacement therapy (RRT) is recommended in fluid-overloaded patients' refractory to diuretics but compelling evidence about its usefulness is still lacking. Continuous RRT (CRRT) is advocated, though not sustained by convincing data, as the preferred modality in hemodynamically unstable SAKI. Diuretics should be avoided in the absence of hypervolemia. Antimicrobial dosing during CRRT needs to be thoroughly reconsidered to assure adequate infection control.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".