Early Start of Dialysis Therapy is Beneficial for Patients with Acute Renal Failure following Cardiac Surgery
Bibliographic record
Abstract
Acute renal failure requiring dialysis therapy after cardiac surgery occurs in 1–5% of patients; however, the optimal timing for the 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 with the timing of decrease of urine volume less than 30 mL/h and other 14 patients who waited to begin dialysis therapy until the level of urine volume of less than 20 mL/h during 14 days. Overall mortality of those patients was 50%. Twelve of 14 patients who received the early intervention survived. In contrast, only 2 of 14 patients in the other group survived. There was a significant difference of p < 0.01 between the two groups. Between the two groups, there were no significant differences in age, sex ratio, the score of APACHE (Acute Physiologic and Chronic Health Evaluation) II, and the levels of serum creatinine at the start of dialysis therapy (2.9 + 0.2 vs. 3.1 + 0.2 mg/dL) as well as in the levels of serum creatinine at admission. The start timing for the treatment of acute renal failure following cardiac surgery would be determined by the decrease of urine volume but not by the levels of serum creatinine. The early start of dialysis therapy might be preferable for the improvement of survival of the patients suffering from 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".