Decision regret and long-term weight evolution following laparoscopic sleeve gastrectomy as bridge to kidney transplantation
Bibliographic record
Abstract
Introduction Laparoscopic sleeve gastrectomy (LSG) is effective for rapid weight loss in kidney transplant (KT) candidates. This study aims to evaluate satisfaction or regret with the decision to undergo LSG in preparation for KT and the long-term durability of this approach to weight loss. Methods From 2012 to 2019, all patients who underwent LSG prior to waitlisting for KT were included. The Decision Regret Scale (DRS) was assessed regarding the decision to undergo LSG before KT. The long-term weight evolution was also collected. Findings Forty-six subjects completed the DRS survey at a median follow-up of 8 years post-LSG: 67% reported absolutely no regret, 22% mild regret, and 11% moderate to strong regret. Successful surgical weight loss was achieved in 36 patients and was significantly associated with lower levels of regret ( p = 0.005). Body mass index reductions after LSG were highly significant compared to baseline values at all time points over 10 years ( p = 0.0001) and remained significantly lower for up to 7 years post-KT. Thirty-two patients received KT, yet this had no significant association with decision regret. Conclusion Laparoscopic sleeve gastrectomy as a pre-transplantation weight loss strategy is associated with very low levels of regret, regardless of the KT status. LSG has demonstrated long-term, durable weight loss.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".