SP267VALIDATING A SCORING TOOL TO PREDICT ACUTE KIDNEY INJURY (AKI) FOLLOWING CARDIAC SURGERY
Bibliographic record
Abstract
Introduction and Aims: Acute kidney injury (AKI) after cardiac surgery is associated with increased mortality. Pre-operative risk scores may simplify identification of AKI patients to improve patient management. Current validated scoring tools are used to predict AKI requiring dialysis (AKI-D); less is known about whether these tools can predict less severe forms of AKI. The purpose of this study was to evaluate the Cleveland Clinic scoring tool in predicting both AKI-D and less severe AKI in patients in the Regina Qu'Appelle Health Region (RQHR). Methods: Data on risk factors for AKI were collected from a retrospective chart review of 2343 cardiac surgery patients between 2007 and 2011 in the RQHR. The primary outcomes were AKI and AKI-D. AKI was defined as an increase of serum creatinine 50% over baseline during the post-operative period in hospital; AKI-D was defined as patients who had at least one post-operative dialysis session in hospital. We assessed the performance of a modified version of the Cleveland Clinic tool using all available risk factor data.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".