47 A Preoperative Renal Scoring Index to Predict Postoperative Acute Renal Failure in Cardiac Surgery Patients
Bibliographic record
Abstract
Purpose: Postoperative acute renal failure (POARF) is one of the most serious complications of cardiac surgery, developing in 5–30% of patients. POARF is an abrupt reduction in renal function, as evidenced by an increase in serum creatinine and a decrease in glomerular filtration rate (GFR). To date, there is no precise method of identifying patients at risk of developing POARF. Accurately identifying patients at risk and implementing preoperative renal protective strategies may reduce the incidence of POARF. The objective of this retrospective chart review was to determine the accuracy of a preoperative renal scoring index in predicting POARF in cardiac surgery patients who underwent facilitated recovery techniques. Methods: Data were collected via a retrospective chart review on patients, who had cardiac surgery at a single site in Ontario, Canada between February and March, 2008. Data abstraction was done by one author. Inconsistencies in abstracted data were clarified by a second author. The renal scoring index included information on GFR, ejection fraction, diabetes, previous cardiac surgery, urgency of surgery, other procedures and preoperative use of the intra-aortic balloon pump. Other preoperative [age, sex, weight, height, BMI and co-morbid factors] and intra-operative variables [type of surgery, aortic cross clamp time, cardiopulmonary bypass (CPB) and OR duration] were included. The RIFLE classification was used to identify POARF [risk, injury, failure, loss and end stage kidney disease (ESKD)]. Statistical analyses were undertaken using SPSS ® (version 15).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".