Impact of Preoperative Renal Dysfunction on Cardiac Surgery Results
Bibliographic record
Abstract
Results of cardiac surgery were analyzed using a database that included plasma creatinine levels in 2,214 patients, of whom 507 had preoperative renal dysfunction (creatinine clearance < 0.9 mL x s(-1) x m(-2)). Logistic regression and propensity score analyses found preoperative renal dysfunction to be an independent predictor of morbidity and mortality. Plotting preoperative creatinine clearance against morbidity and mortality revealed an exponential increase in morbidity and mortality when preoperative creatinine clearance was < 0.84 mL x s(-1) x m(-2). Patients were stratified for age, operative procedure, and comorbidity. In all stratified groups, preoperative creatinine clearance < 0.84 mL x s(-1) x m(-2) was associated with similar exponential increases in morbidity and mortality. In patients with preoperative renal dysfunction, elevated plasma creatinine levels persevered for 6 months postoperatively. Dialysis beyond postoperative day 10 was required in < 2% of patients with preoperative plasma creatinine of 160-200 micro mol x L(-1) and in 5% in those with creatinine > 200 micro mol x L(-1) (p < 0.05). Actuarial survival was significantly reduced (< 90% at 18 months postoperatively) in patients with preoperative renal dysfunction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".