Importance of early dialysis for acute renal failure after an open‐heart surgery
Bibliographic record
Abstract
Coronary artery disease is a major cause of death in patients with a renal dysfunction. Among the patients who undergo coronary artery bypass grafting, renal dysfunction is known to be a major predictor of in-hospital and out-of-hospital mortality. From 2004 to 2007, we performed elective open-heart surgeries on 2380 patients in whom there was no primary renal failure. Of those patients, only 185 in whom acute renal failure (ARF) was developed were included in the study. The patients were divided into 2 groups: a late dialysis group (n=90) and an early dialysis group (n=95). The mean age of the patients was 62.3+/-6.4 in the late dialysis group and 64.5+/-5.2 in the early dialysis group. There were 32 female and 58 male patients in the late dialysis group and 36 female and 59 male patients in the early dialysis group. Acute renal failure developed only in 185 patients out of 2380 open-heart surgery patients. The overall mortality in the 2380 open-heart surgery patients was 1.97%. Mortality among the ARF patients was 5.9%. However, there was no significant difference in hospital mortality between the 2 groups. Major complications, such as postoperative pneumonia, prolonged ventilation time, arrhythmia, the number of times postoperative hemodialysis was performed, development of chronic renal failure, time spent in the intensive care unit and the period of hospitalization, sepsis, and low cardiac output, were significantly higher in the late dialysis group. There was no difference in mortality between the 2 groups. Early dialysis for open-heart surgery patients who develop ARF postoperatively does not decrease mortality. However, it decreases morbidity, the amount of time spent in intensive care, and the period of hospitalization and thus reduces patient costs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".