Serum Cystatin C as an Index of Early Detection of Acute Kidney Injury in Children With Severe Malaria
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) in severe malaria is a major cause of morbidity and mortality. It is often diagnosed using serum creatinine (Scr), which has several limitations. A test for early confirmation of AKI devoid of these limitations is therefore required. Serum cystatin C is therefore suggested as an early screening tool in diagnosing AKI in severe malaria. This study aimed at determining the accuracy of serum cystatin C in the early diagnosis of AKI in severe malaria. Methods: This was a cross-sectional study. Children with World Health Organization (WHO)-defined severe malaria were recruited and AKI was defined using the Kidney Disease: Improving Global Outcomes (KDIGO) and WHO criteria as well as a serum cystatin C level of > 0.95 mg/L. The Scr and cystatin C levels were done and data were analyzed with a P-value less than 5% considered significant. Results: A total of 126 children aged 1 - 15 years comprising 70 (55.6%) males with a male-to-female ratio of 1.25:1 were studied. The prevalence of AKI was 38.9% using serum cystatin C, 23.8% using KDIGO and 11.9% using WHO criteria with statistically significant difference (P = 0.001). The mean serum cystatin C was 0.74 (0.59) mg/L. The sensitivity, specificity, and negative predictive value of cystatin C in the diagnosis of AKI in severe malaria were 80.0%, 73.9%, and 92.2%, respectively using the KDIGO definition of AKI. Conclusion: Serum cystatin C has a high sensitivity, specificity and negative predictive value and can be used to screen children for early detection of AKI in severe malaria. World J Nephrol Urol. 2024;13(1):19-25 doi: https://doi.org/10.14740/wjnu445
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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) | 0.000 | 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 | 0.001 |
| 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".