VALUE OF RENAL DOPPLER IN DIFFERENTIATING OBSTRUCTIVE FROM NON-OBSTRUCTIVE HYDRONEPHROSIS BY MEASUREMENT OF RENAL ARTERY RESISTIVE INDEX
Bibliographic record
Abstract
Background: Doppler Ultrasound has provided a new insight into the physiology of kidney, enabling detection of subtle renal blood flow changes associated with various pathophysiological conditions. Apart from being non-ionizing and non-invasive, it has been reported to help in differentiating obstructive from nonobstructive hydronephrosis (HN) by renal arterial resistive index (RI) measurements. Objective: The main aim of our study to determine the utility of RI in distinguishing between obstructive and non-obstructive hydronephrosis (HN) in pediatric age group. Methodology: This a case control study was carried out on a sample of 20 youngsters diagnosed with hydronephrosis, along with 10 individuals who were selected as healthy controls. The participants were recruited from Hussien& Bab Alshieria Al-Azhar University Hospitals. The research was carried out from December 2022 to September 2023. All the studied cases were subjected to comprehensive medical history, thorough physical examination, laboratory and radiological assessment. 20 patients were taken after applying including criteria and undergone Renal Doppler assessment for evaluation of hydronephrosis and differentiating obstructive from non-obstructive HN. Results: Mean Ri (0.740±0.05) for obstructive HN was significantly higher than the mean RI (0.31±0.05) for non-obstructive HN. The determination of RI useful for differentiating obstructive from non-obstructive HN. Also, the mean venous impedance (0.31±0.02) was significantly higher in obstructive HN than non-obstructive HN. Conclusion: The RI of the renal artery at hilum significantly high in obstructive HN so, can be effectively used to distinguish obstructive from non-obstructive hydronephrosis.
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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 | 0.004 |
| 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".