Cardiac surgery-associated acute kidney injury
Bibliographic record
Abstract
Acute kidney injury develops in up to 30% of patients who undergo cardiac surgery, with up to 3% of patients requiring dialysis. The requirement for dialysis after cardiac surgery is associated with an increased risk of infection, prolonged stay in critical care units and long-term need for dialysis. The development of acute kidney injury is independently associated with substantial short- and long-term morbidity and mortality. Its pathogenesis involves multiple pathways. Haemodynamic, inflammatory, metabolic and nephrotoxic factors are involved and overlap each other leading to kidney injury. Clinical studies have identified predictors for cardiac surgery-associated acute kidney injury that can be used effectively to determine the risk for acute kidney injury in patients undergoing cardiac surgery. High-risk patients can be targeted for renal protective strategies. Nonetheless, there is little compelling evidence from randomized trials supporting specific interventions to protect or prevent acute kidney injury in cardiac surgery patients. Several strategies have shown some promise, including less invasive procedures in those at greatest risk, natriuretic peptide, fenoldopam, preoperative hydration, preoperative optimization of anaemia and postoperative early use of renal replacement therapy. The efficacy of larger-scale trials remains to be confirmed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".