The Association of obesity with vascular complications after liver transplantation
Bibliographic record
Abstract
Because of the growing number of obese patients undergoing liver transplantation (LT), it is important to investigate the impact of obesity on post-transplant outcomes. Vascular complications are rare, but serious causes of morbidity and mortality after LT. It is not known if pre-transplant obesity is associated with an increased incidence of post-LT vascular complications. Medline, Embase, and Cochrane Library databases were searched in September 2017. The primary outcome was the impact of obesity on the vascular complication rate in adult LT recipients. Survival and biliary complications rates were also analyzed. Risk ratios (RRs) and 95% confidence intervals (CIs) were calculated to compare pooled data between groups with a body mass index (BMI) ≥ 30 kg/m 2 and < 30 kg/m 2 . Six retrospective cohort studies with a total of 987 patients with a BMI ≥ 30 kg/m 2 (high BMI group) and 2911 patients with a BMI < 3 0 kg/m 2 (control group) were included in the analysis. All studies had Newcastle-Ottawa Scale scores ≥4. The vascular complication rates were similar between the high BMI group and control group (RR = 1.13, 95% CI: 0.87–1.47, P = 0.27), as were the patient survival, graft survival, and biliary complication rates. In subgroup analysis, there was no difference in the vascular complication rates between BMI ≥ 35 vs. BMI < 25 kg/m 2 ; BMI 30–35 vs. BMI 18–25 kg/m 2 ; BMI ≥ 30 vs. BMI 18–25 kg/m 2 ; and BMI ≥ 35 vs. BMI < 35 kg/m 2 . No difference was found in subgroup analysis when BMI was adjusted for ascites. However, recipients whose primary disease was alcoholic liver disease, those with a BMI ≥ 30 kg/m 2 had higher incidence of vascular complications than those with a BMI < 30 kg/m 2 (RR = 1.55, 95% CI: 1.07–2.25, P = 0.02) . BMI does not affect incidence of vascular complications after LT. High pre-transplant BMI is not a risk factor for patient survival and biliary complications after LT.
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 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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".