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 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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".