The association between skin-to-vessel distance and surgical complications in renal transplantation
Bibliographic record
Abstract
INTRODUCTION: Obesity is common among renal transplant recipients and increases the risk of perioperative complications. This study evaluated whether the distance from the skin to the external iliac vessels (SVD) is associated with adverse surgical outcomes in renal transplant recipients. METHODS: A retrospective cohort study of 167 consecutive patients with preoperative cross-sectional imaging who underwent renal transplantation was conducted at a single center. SVD was measured as the distance from the skin to the anterior edge of the external iliac vein at its bifurcation through the musculoaponeurotic layer of the transversus abdominis and oblique muscles. The primary outcome was the rate of postoperative complications, classified by the Clavien-Dindo system. RESULTS: SVD was associated with wound dehiscence (area under the curve [AUC] 0.696, 95% confidence interval [CI] 0.55-0.84, p=0.007) and wound complications (AUC 0.719, 95% CI 0.60-0.84, p<0.001). Using an SVD threshold of ≥19 cm, we observed an overall accuracy of 87.4% for predicting wound dehiscence and 85.6% for any wound complication. The retrospective, single-center design and absence of standardized criteria for CT imaging are inherent limitations that can introduce several biases. CONCLUSIONS: SVD is associated with adverse perioperative outcomes in renal transplantation. Given the indication for preoperative imaging only in high-risk patients, prospective data with a more general renal transplant population is warranted to further evaluate SVD.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".