Comparison of Clavien–Dindo classification and comprehensive complication index in patients undergoing simultaneous pancreas-kidney transplantation
Bibliographic record
Abstract
BACKGROUND Detailed data on the relation of post-operative complications with clinical outcomes after simultaneous pancreas-kidney (SPK) transplantation is lacking. AIM To compare Clavien-Dindo classification (CDC) and comprehensive complication index (CCI) in predicting outcomes after SPK. METHODS Data for patients undergoing SPK between 1999-2019 were analyzed. Information on recipients’ baseline characteristics, peri-operative management and post-operative complications were collated. Length of hospital stay (LOS) was the primary study outcome, and the associations with CDC and CCI were evaluated using Spearman’s (ρ) correlation coefficients. RESULTS In the study period, data were available for 128 patients (female n = 44, 34.4%). Sixty-nine patients had at least one complication with the highest CDC grade of I, II, III, and IV in 8 (6.3%), 22 (17.2%), 32 (25%), and 7 (5.5%) patients, respectively. The mean LOS was 21.4 ± 17.7 days. Both classification systems were correlated with LOS, yet CCI was stronger (Spearman’s ρ: 0.694 vs 0.602, P < 0.001). Female patients (P = 0.019) and patients with pre-transplant cardiovascular events (P = 0.02) had longer LOS. After adjusted multivariable analysis, the link between LOS and both the CDC and CCI remained relevant. CCI had a superior fit compared to CDC (r 2 = 0.729 vs r 2 = 0.481), with every 10 CCI points being associated with a 5.27 day (P < 0.001) increased LOS. CONCLUSION This study showed that the CCI was better linked with LOS compared to CDC and might represent a useful score to evaluate the overall burden of postoperative complications in patients undergoing SPK.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".