A simple method to estimate renal volume from computed tomography
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Bibliographic record
Abstract
INTRODUCTION: Renal parenchymal volume can be used clinically to estimate differential renal function. Unfortunately, conventional methods to determine renal volume from computed tomography (CT) are time-consuming or difficult due to software limitations. We evaluated the accuracy of simple renal measurements to estimate renal volume as compared with estimates made using specialized CT volumetric software. METHODS: We reviewed 28 patients with contrast-enhanced abdominal CT. Using a standardized technique, one urologist and one urology resident independently measured renal length, lateral diameter and anterior-posterior diameter. Using the ellipsoid method, the products of the linear measurements were compared to 3D volume measurements made by a radiologist using specialized volumetric software. RESULTS: LINEAR KIDNEY MEASUREMENTS WERE HIGHLY CONSISTENT BETWEEN THE UROLOGIST AND THE UROLOGY RESIDENT (INTRACLASS CORRELATION COEFFICIENTS: 0.97 for length, 0.96 for lateral diameter, and 0.90 for anterior-posterior diameter). Average renal volume was 170 (SD: 36) cm(3) using the ellipsoid method compared with 186 (SD 37) cm(3) using volumetric software, for a mean absolute bias of -15.2 (SD 15.0) cm(3) and a relative volume bias of -8.2% (p < 0.001). Thirty-one of 56 (55.3%) estimated volumes were within 10% of the 3D measured volume and 54 of 56 (96.4%) were within 30%. CONCLUSION: Renal volume can be easily approximated from contrast-enhanced CT scans using the ellipsoid method. These findings may obviate the need for 3D volumetric software analysis in certain cases. Prospective validation is warranted.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it