DOES PRETRANSPLANT OBESITY AFFECT THE OUTCOME IN KIDNEY TRANSPLANT RECIPIENTS?
Bibliographic record
Abstract
P361 Aims: The effect of obesity on renal transplant outcome remains unclear due to conflicting published studies. The purpose of this study was to assess whether obesity affects the outcome in renal transplant patients. Methods: We retrospectively analyzed 33 obese (BMI >30; Mean = 34.1±3.68; Group I) and 35 non-obese (BMI ≤ 30; Mean= 23.6 ± 3.18; Group II) renal transplants performed at our centre between March 1999 to December 2002. These two groups were well matched with respect to age, sex, donor source, hypertension, diabetes, ischemic heart disease, hyperlipidemia, native kidney disease (PCKD, 6 vs. 4; Diabetic, 5 vs. 4; Glomerulonephritis, 6 vs. 7; FSGS, 2 vs. 2 and IgA, 2 vs. 7), HLA mismatch and immunosuppressants medications (Neoral, 21 vs. 25; tacrolimus, 11 vs. 10; Cellcept, 28 vs. 31; Prednisone, 33 vs. 35; ATG, 7 vs. 8; Basiliximab, 14 vs. 13 and Rapamycin, 5 vs. 2, Group I and II respectively). Follow up was from 7 months to 4.4 years. Results: Significant differences were noted in operating time, wound infection, perinephric hematoma, lymphocele and number of hospital days as shown in Table. There were no significant difference between the 2 groups in the incidence of wound dehiscence, deep vein thrombosis, pulmonary embolism, atelectasis, urine leak, delayed graft function, acute rejection rate, and the following post-transplant variables: diabetes mellitus, myocardial infarction, hyperlipidemia, hypertension and incisional hernia.FigureConclusions: We conclude that obesity significantly increases operating time, wound complications and hospitalizations.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".