A simple method to estimate renal volume from computed tomography
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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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".