Early start on continuous hemodialysis therapy improves survival rate in patients with acute renal failure following coronary bypass surgery
Bibliographic record
Abstract
Acute renal failure requiring dialysis therapy after cardiac surgery occurs in 1% to 5% of patients; however, the optimal timing for initiation of dialysis therapy still remains undetermined. To assess the validity of early start of dialysis therapy, we studied the comparative survival between 14 patients who started to receive dialysis therapy when urine volume decreased to less than 30 mL/hr and another group of 14 patients who waited to begin dialysis therapy until the level of urine volume was less than 20 mL/hr for 14 days following coronary bypass graft surgery. Twelve of 14 patients who received early intervention survived. In contrast, only 2 of 14 patients in the late-dialysis group survived. There was a significant difference in survival between the two groups (p < 0.01). There were no significant differences between the two groups with respect to age, sex ratio, the APACHE (Acute Physiologic and Chronic Health Evaluation) II score, and the levels of serum creatinine at the start of dialysis therapy (2.9 +/- 0.2 mg/dL vs. 3.1 +/- 0.2 mg/dL), as well as the levels of serum creatinine at admission. We propose that the timing of the start for treatment of acute renal failure following cardiac surgery should be determined by the decrease of urine volume and not the levels of serum creatinine. Early start of dialysis therapy may help improve the survival of patients with acute renal failure following cardiac surgery.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".