Distribution of Iliac Artery Calcification on Unenhanced Computed Tomography Scans Performed on Potential Recipients Prior to Renal Transplantation
Bibliographic record
Abstract
Purpose: To investigate whether a significant difference exists between the calcification of the common iliac arteries (CIAs) and the external iliac arteries (EIAs) and test for associations between clinical factors and the distribution of calcification. Methods: A retrospective review of renal transplant candidates who underwent a routine preoperative unenhanced computed tomography yielded 214 patients. Agatston scores for the patients’ left CIA, left EIA, right CIA, and right EIA were assigned. A retrospective search of patient records screened for 5 clinical factors (diabetes, hypertension, coronary artery disease [CAD], smoking, and dialysis). Data were assessed using a 2-sided t test, odds ratio, and a multivariate linear regression calculated through generalized estimating equation (GEE). Results: The log-transformed Agatston scores in the CIA were found to be significantly greater than that in the EIA ( t = 9.57, P < .0001), with a mean difference of 1.5078 (95% confidence interval: 1.1962-1.8194), indicating relative EIA sparing. There were no significant differences in calcification between the right and left sides. Generalized estimating equation found that CAD and smoking demonstrated independent positive associations with EIA sparing (GEE = 2.6464 [ P = .0197] and 1.9092 [ P = .0470], respectively). Age was also significantly associated and indicated that EIA sparing remained relatively constant throughout patients’ lives (GEE = 1.0711 [ P < .0001]). Conclusion: This study has demonstrated statistically significant EIA sparing in end-stage renal disease patients and identified CAD and smoking as associated factors. This phenomenon warrants further investigation into its biological mechanisms and the impact of EIA sparing on outcomes following transplants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".