Ameliorating acute kidney injury following cardiac surgery: do high dose perioperative statins play a role?
Bibliographic record
Abstract
Acute kidney injury (AKI) is relatively common in the patients undergoing cardiac surgery. Over the past decade, several consensus definitions (namely the RIFLE, AKIN and KIDGO) have been developed to provide uniform criteria for the diagnosis of AKI, in order to facilitate comparisons between studies and the development of quantitative research (1-3). The incidence of AKI after cardiac surgery differs slightly depending on the classification criteria, ranging from 31–46% (3-5). It is well known that patients who developed AKI after cardiac surgery are at an increased risk of short-term and long-term morbidity and mortality. These patients tend to have higher mortality rates, prolonged ICU and hospital stay (5-7). These patients also have an increased risk of subsequent developing chronic kidney disease (CKD), which is associated with a higher long-term mortality (8).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".