Comparative Efficacy of Serum Creatinine and Microalbuminuria in Detecting Early Renal Injury in Asphyxiated Babies in Calabar, Nigeria
Bibliographic record
Abstract
Background: Microalbuminuria and serum creatinine are markers of acute kidney injury. Birth asphyxia is responsible for 50% of all newborn deaths and acute non-oliguric kidney injury is one of such complications. This study was undertaken to determine the efficacy of serum creatinine and microalbuminuria for the detection of early renal lesion in severely asphyxiated babies in Calabar, Nigeria. Materials and Method: This prospective cross-sectional investigational study was undertaken among severely asphyxiated babies admitted into the newborn units of the University of Calabar Teaching Hospital (UCTH), Calabar, Nigeria. Standard method for blood collection and determination of urea, electrolytes were used. Micral-test strips were used on samples negative only for albumin after using urine dipstick. Color comparison was done with the standardized color scale on test strip container after 5 minutes. Results: Fifty term newborn babies were enrolled, their serum electrolytes, creatinine and creatinine clearance were essentially normal. Six (12%) babies had positive microalbuminuria, while 44(88%) had negative microalbuminuria with specificity and negative predictive values of 100% and 88% respectively. Conclusion: Microalbuminuria was not useful for early detection of acute renal failure in babies with severe birth asphyxia, but further studies are recommended.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".