Surgical Complications after Right Hepatectomy for Live Liver Donation: Largest Single-Center Western World Experience
Bibliographic record
Abstract
Abstract The authors assessed the incidence, management, and risk factors for postoperative complications after right lobe (RL) live donor hepatectomy in a high-volume center in North America. All donors undergoing an RL live donor hepatectomy between 2000 and 2017 at our institution were included. The primary outcome was the development of complications (both medical and surgical). Predictors of postoperative complications were determined by logistic regression. A total of 587 patients underwent RL live donor hepatectomy. Among those, 187 postoperative complications were diagnosed in 141 (24%) patients. One patient had >90-day morbidity, and there were no donor deaths. Overall complications were significantly higher in the first era, 2000 to 2008 (81 [57.4%]) versus the second era, 2009 to 2017 (60 [42.6%]) (p = 0.01). On multivariate analysis, the only predictor of postoperative complications was the center volume of RL live donor hepatectomy in the previous 12 months with an odds ratio of 0.97 (95% confidence interval: 0.95–0.99). In conclusion, increasing center volume is associated with lower rates of postoperative complications after RL living liver donation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".