Faculty Opinions recommendation of Four hour creatinine clearance is better than plasma creatinine for monitoring renal function in critically ill patients.
Bibliographic record
Abstract
INTRODUCTION: Acute kidney injury (AKI) diagnosis is based on an increase in plasma creatinine, which is a slowly changing surrogate of decreased glomerular filtration rate. We investigated whether serial creatinine clearance, a direct measure of the glomerular filtration rate, provided more timely and accurate information on renal function than serial plasma creatinine in critically ill patients.METHODS: Serial plasma creatinine and 4-hour creatinine clearance were measured 12-hourly for 24 hours and then daily in 484 patients. AKI was defined either as > 50% increase in plasma creatinine from baseline, or > 33.3% decrease in creatinine clearance. The diagnostic and predictive performance of the two AKI definitions were compared.RESULTS: Creatinine clearance decrease diagnosed AKI in 24% of those not diagnosed by plasma creatinine increase on entry. These patients entered the ICU sooner after insult than those diagnosed with AKI by plasma creatinine elevation (P = 0.0041). Mortality and dialysis requirement increased with the change in creatinine clearance-acute kidney injury severity class (P = 0.0021). Amongst patients with plasma creatinine < 1.24 mg/dl on entry, creatinine clearance improved the prediction of AKI considerably (Net Reclassification Improvement 83%, Integrated Discrimination Improvement 0.29). On-entry, creatinine clearance associated with AKI severity and duration (P < 0.0001) predicted dialysis need (area under the curve: 0.75) and death (0.61). A > 33.3% decrease in creatinine clearance over the first 12 hours was associated with a 2.0-fold increased relative risk of dialysis or death.CONCLUSIONS: Repeated 4-hour creatinine clearance measurements in critically ill patients allow earlier detection of AKI, as well as progression and recovery compared to plasma creatinine.TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN012606000032550. PMID: 22713519
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.129 | 0.090 |
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".