Predictors of Low Cardiac Output Syndrome After Isolated Aortic Valve Surgery
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Low cardiac output syndrome (LCOS), defined as the need for postoperative intraaortic balloon pump or inotropic support for >30 minutes in the intensive care unit, remains a relatively common complication of aortic valve (AV) surgery. The aim of this study is to identify the preoperative predictors of LCOS in patients undergoing isolated AV surgery. METHODS AND RESULTS: We conducted a retrospective review of data prospectively entered into an institutional database. Between 1990 and 2003, 2255 patients underwent isolated AV surgery with no other concomitant cardiac surgery. The independent predictors of LCOS and operative mortality (OM) were determined by stepwise logistic regression analysis. The overall prevalence of LCOS was 3.9%. The independent predictors of LCOS were (odds ratio in parentheses) renal failure (5.0), earlier year of operation (4.4), left ventricular ejection fraction <40% (3.6), shock (3.2), female gender (2.8), and increasing age (1.02). Overall OM was 2.9%. The OM was higher in patients who experienced LCOS (38% versus 1.5%; P<0.001). The independent predictors of mortality were (odds ratio in parentheses) preoperative renal failure (8.3), urgency of surgery (3.4), previous stroke (2.9), congestive heart failure (2.6), previous cardiac surgery (2.3), hypertension (1.7), and small AV size (1.3). CONCLUSIONS: Low-output syndrome is associated with significantly increased morbidity and mortality. Novel strategies to preserve renal function, optimization of preexisting heart failure symptoms, and avoidance of prosthesis-patient mismatch may reduce the incidence of LCOS and lead to improved results after AV surgery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 it