Fluid accumulation, recognition and staging of acute kidney injury in critically-ill patients
Bibliographic record
Abstract
INTRODUCTION: Serum creatinine concentration (sCr) is the marker used for diagnosing and staging acute kidney injury (AKI) in the RIFLE and AKIN classification systems, but is influenced by several factors including its volume of distribution. We evaluated the effect of fluid accumulation on sCr to estimate severity of AKI. METHODS: In 253 patients recruited from a prospective observational study of critically-ill patients with AKI, we calculated cumulative fluid balance and computed a fluid-adjusted sCr concentration reflecting the effect of volume of distribution during the development phase of AKI. The time to reach a relative 50% increase from the reference sCr using the crude and adjusted sCr was compared. We defined late recognition to estimate severity of AKI when this time interval to reach 50% relative increase between the crude and adjusted sCr exceeded 24 hours. RESULTS: The median cumulative fluid balance increased from 2.7 liters on day 2 to 6.5 liters on day 7. The difference between adjusted and crude sCr was significantly higher at each time point and progressively increased from a median difference of 0.09 mg/dL to 0.65 mg/dL after six days. Sixty-four (25%) patients met criteria for a late recognition to estimate severity progression of AKI. This group of patients had a lower urine output and a higher daily and cumulative fluid balance during the development phase of AKI. They were more likely to need dialysis but showed no difference in mortality compared to patients who did not meet the criteria for late recognition of severity progression. CONCLUSIONS: In critically-ill patients, the dilution of sCr by fluid accumulation may lead to underestimation of the severity of AKI and increases the time required to identify a 50% relative increase in sCr. A simple formula to correct sCr for fluid balance can improve staging of AKI and provide a better parameter for earlier recognition of severity progression.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".