Correlation Between Renal Dimensions and Anthropometric Indices Using Computed Tomography in Adults Without Known Renal Disease: A Cross-Sectional Prospective Study
Bibliographic record
Abstract
Background: Assessing renal volume as a potential indicator of renal function and related disorders is valuable for clinical decision-making. Computed tomography (CT) can accurately estimate actual kidney size. Objectives: This study aimed to evaluate the relationship between anthropometric parameters and renal dimensions measured by CT. Methods: Renal CT scan evaluations were performed on 634 individuals (308 males and 326 females) who had undergone abdominopelvic CT scans for indications unrelated to renal disease. Renal parameters, including length, width, depth, volume, and cortex length, were measured. Results: The mean age of participants was 53.5 ± 13.7 years (range: 18 - 86 years). Renal dimensions in males were larger than those in females. Additionally, the left kidney showed larger dimensions than the right kidney in both genders. Renal dimensions increased with age initially, but began to decrease after the sixth decade of life. A significant negative correlation was found between age and renal length, cortex, and left renal volume. In contrast, a significant positive correlation was observed between weight and both renal depth, length, volume, and left renal cortex, as well as between height and both renal length and volume on both sides. All dimensions except renal length were greater with increasing Body Mass Index (BMI). Conclusions: The results indicate a significant correlation between kidney dimensions and various anthropometric factors such as age, weight, height, and BMI. These findings provide valuable insights into kidney dimensions measured on CT scans, potentially aiding in the diagnosis and treatment of kidney diseases.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".