Prenatal diagnosis of horseshoe kidney by measurement of the renal pelvic angle
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the usefulness of measurement of the angle between bilateral renal pelves on axial views in the prenatal ultrasonographic diagnosis of horseshoe kidney. METHODS: We retrospectively measured the renal pelvic angle in 19 fetuses with horseshoe and 20 fetuses with normal kidneys in the second and third trimesters. Renal pelvic angle was defined as the angle between the long axis of the renal pelves on the axial view of the abdomen. We compared the renal pelvic angles of horseshoe and normal kidneys with unpaired t-test. Taking 140 degrees as a cut-off value, we calculated the sensitivity, specificity and accuracy of pelvic angle measurement for the prenatal diagnosis of horseshoe kidney. RESULTS: The mean pelvic angles in the fetuses with horseshoe kidney were 116 degrees and 110 degrees in the second and third trimester, respectively. In the normal fetuses, the equivalent angles were 172 degrees and 161 degrees. The difference between the two groups was statistically significant (P < 0.01). Using 140 degrees as the discriminating criterion, the sensitivity, specificity and accuracy of renal pelvic angle measurement for the prenatal diagnosis of horseshoe kidney were all 100%. Fifteen of 19 fetuses with horseshoe kidney had no other abnormality. Four (21%) fetuses had severe complex abnormalities which were associated with trisomy 18 in three cases. CONCLUSION: Observation and measurement of the renal pelvic angle is a simple and useful method in the prenatal diagnosis of the horseshoe kidney.
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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 | 0.006 |
| 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".