Sonographic Evaluation of Maternal Renal Echogenicity in Healthy Pregnant Women in the Niger Delta Region of Nigeria
Bibliographic record
Abstract
BACKGROUND: Increased renal echogenicity is a non-specific ultrasound finding. It may be a normal variation or suggestive of various underlying conditions like renal amyloidosis, chronic kidney disease, sickle cell disease and HIV associated nephropathy (HIVAN). Objective: To study maternal renal echogenicity in normal pregnancy, and explore its relationship with maternal baseline characteristics in our subregion. METHODS: This descriptive, cross-sectional study was conducted in the Obstetrics and Radiology Units of the two tertiary health facilities, one secondary facility and one radio-diagnostic facility, all in Bayelsa State, South-South Nigeria, between March-August 2022. The relationships between maternal renal echogenicity and age, parity and gestational age were explored using Chi-square test of proportion, while with an analysis of variance (ANOVA), the mean difference of age, weight and height between the grades of renal echogenicity was investigated. Kruskal Wallis test was deployed to examine parity in the grades of renal echogenicity. The level of significance was set at p<0.05. RESULTS: The study participants that had Grade 0, 1 and 2 renal echogenicity were 160 (39.7%), 403 (58.3%) and 8 (2.0%), respectively. There were statistically significant relationships between maternal renal echogenicity and maternal age (ꭓ2=36.94; p=0.001), parity (ꭓ2=64.29; p=0.001), gestational age (ꭓ2=16.03; p=0.003) and body mass index (BMI) (ꭓ2 = 45.15; p – 0.001). CONCLUSION: Our study revealed a significant relationship between maternal renal echogenicity in normal pregnancy and maternal baseline characteristics (age, parity, gestational age and weight).
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".