Who may not benefit from continuous renal replacement therapy in acute kidney injury?
Bibliographic record
Abstract
This study aimed to identify factors that may predict early kidney recovery (less than 48 hours) or early death (within 48 hours) after initiating continuous renal replacement therapy (CRRT) in acute kidney injury (AKI) patients. This is a multicenter retrospective observational study of 14 Japanese Intensive care units (ICUs) in 12 tertiary hospitals. Consecutive adult patients with severe AKI requiring CRRT admitted to the participating ICUs in 2010 (n=343) were included. Patient characteristics, variables at CRRT initiation, settings, and outcomes were collected. Patients were grouped into early kidney recovery group (CRRT discontinuation within 48 hours after initiation, n=52), early death group (death within 48 hours after CRRT initiation, n=52), and the rest as the control group (n=239). The mean duration of CRRT in the early kidney recovery group and early death group was 1.3 and 0.9 days, respectively. In multivariable regression analysis, in comparison with the control group, urine output (mL/h) (odds ratio [OR]: 1.02, 95% confidence interval [CI]: 1.01-1.03), duration between ICU admission to CRRT initiation (days) (OR: 0.65, 95% CI: 0.43-0.87), and the sepsis-related organ failure assessment score (OR: 0.87, 95% CI; 0.78-0.96) were related to early kidney recovery. Serum lactate (mmol/L) (OR: 1.19, 95% CI: 1.11-1.28), albumin (g/dL) (OR: 0.52, 95% CI: 0.28-0.92), vasopressor use (OR: 3.68, 95% CI: 1.37-12.16), and neurological disease (OR: 9.64, 96% CI: 1.22-92.95) were related to early death. Identifying AKI patients who do not benefit from CRRT and differentiating such patients from the study cohort may allow previous and future studies to effectively evaluate the indication and role of CRRT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".