Comparison of serum levels of Cystatin-C and traditional renal biomarkers for the early detection of pre-hypertensive nephropathy.
Bibliographic record
Abstract
OBJECTIVE: To compare serum Cystatin-C and serum creatinine levels along with estimated glomerular filtration rate of apparently healthy people of South Asian descent with pre-hypertension to determine which is better in detecting reversible renal dysfunction. METHODS: :The comparative cross-sectional study was conducted at the Army Medical College, Rawalpindi, Pakistan, in 2013-14, and comprised apparently normal healthy male and female volunteers. The subjects were divided into normotensive group 1 and pre-hypertensive group 2. Serum Cystatin-C levels were measured by sandwhich enzyme-linked immunosorbent assay technique whereas serum creatinine levels were measured by Jaffe's procedure. Glomerular filtration rate estimation was done by using standard equations. SPSS 20 was used for data analysis. RESULTS: Of the 78 subjects, 39(50%) were in normotensive group 1 and 39(50%) in the pre-hypertensive group 2. The mean age was 38.74 } 5.71 years in group 1 and 38.07 } 3.84 years in group 2. Serum Cystatin-C levels were higher in group 2 than in group 1(p= 0.0001), whereas serum creatinine levels manifested no statistical difference between the groups (p=0.106). Estimated glomerular filtration rate based on Cystatin-C significantly decreased in group 2 than in group 1 (p=0.0001). Serum Cystatin-C displayed a significant positive correlation and estimated glomerular filtration rate based on Cystatin-C negative correlation with the rising blood pressure values (p=0.0001).Serum Cystatin-C reflected a very high sensitivity and specificity at a cutoff value of 0.77 mg/l compared to serum creatinine. CONCLUSIONS: Serum Cystatin-C and Estimated glomerular filtration based on rate Cystatin-C appeared to be better renal biomarkers in the detection of pre-hypertensive nephropathy.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".