Baseline urinary KIM-1 concentration in detecting acute kidney injury should be interpreted with patient pre-existing nephropathy
Bibliographic record
Abstract
OBJECTIVES: To determine whether pre-existing nephropathy impacts urinary KIM-1 levels, urinary KIM-1 were measured in patients with normal kidney filtration function but either with or without proteinuria. The reference intervals of urinary KIM-1 in adults with normal kidney filtration function but without urine proteinuria were established. DESIGN AND METHODS: 188 urine samples were obtained from adults with normal kidney filtration. 83 of the 188 showed negative urine protein, erythrocytes and leucocytes were used as normal controls. The remaining 105 samples showed at least one abnormal result suggesting possible pre-existing nephropathy. Urinary KIM-1 concentrations were measured using an enzyme-linked immunosorbent assay. Urinary KIM-1 was normalized with urine creatinine concentration. The reference interval for urinary KIM-1 was determined by non-parametric methodology on 147 individuals. RESULTS: The results showed significantly increased urinary KIM-1 concentration in protein positive (protein +, erythrocyte +/-, leucocyte+/-) samples compared to controls (protein-, erythrocyte -, leucocyte -). Urinary KIM-1 concentrations were significantly higher when proteinuria was at trace concentration (0.25 g/L) and correlated with the severity of proteinuria. The creatinine normalized urinary KIM-1 was significantly higher when urine protein was 1 + to 3+ (0.75-5 g/L). The reference interval for urinary KIM-1 was 0.00 (90%CI: 0-0) to 4.19 (90%CI: 3.11-5.62) μg/L, and for creatinine normalized urinary KIM-1 0.00 (90%CI: 0-0) to 0.58 (90%CI: 0.44-0.74) μg/mmol. CONCLUSIONS: Baseline urinary KIM-1 concentrations were increased when there was detectable urine protein and correlated with its severity. The urinary KIM-1 concentrations should be interpreted with consideration of urine protein levels in individual patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".