Multidetector computed tomography angiography study of the renal arterial vasculature anatomy and its variations in a Bulgarian adult population
Bibliographic record
Abstract
Abstract Purpose: Renal arterial anatomy has a great clinical importance during surgical and endovascular procedures. However, comprehensive data on the renal arterial variations in the Bulgarian population has not yet been provided. The aim of this study was to conduct detailed research about the normal anatomy and variations of the renal arteries in the Bulgarian population. Methods: Five hundred sixty one patients underwent contrast-enhanced multidetector computed tomography scans for the period 2016-2021. The images were retrospectively reviewed. Number, branching pattern, origin level and course of the renal arteries were noted. Data was categorized on the basis of laterality, gender and symmetry. Results: Only 46.3% of the patients exhibited normal renal arterial anatomy. Variations were observed in 301 patients (53.7%). The most common variant was the presence of accessory renal arteries (ARA), discovered in 41.2% of the subjects. There was no significant difference based on gender and laterality (p>0.05). Hilar ARA (72.6%) were significantly more common than polar ARA (p<0.001). The most common origin location of main renal arteries and ARA was the aorta, followed by the common iliac arteries. Early division was observed in 21.7% of the patients, significantly more common on the right. Precaval course was found in 0.5% of the right main renal arteries and in 30% of ARA and the difference was significant (p<0.001). Conclusion: These results show novel insight into the prevalence of renal arterial variations in Bulgarian population. Anatomic renal vasculature variants are common therefore awareness is crucial for the success of surgical and interventional procedures.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".