Bibliographic record
Abstract
PURPOSE OF REVIEW: Obesity has reached epidemic proportions in the United States. It is a risk factor for developing, among others, heart disease, stroke, type 2 diabetes, and chronic kidney disease (CKD), and thus a major public health concern and driver of healthcare costs. Although the prevalence of obesity in the CKD/end-stage kidney disease population is increasing, many obese patients are excluded from the benefit of kidney transplant based on their BMI alone. For this reason, we sought to review the experience thus far with kidney transplantation in obese patients and associated outcomes. RECENT FINDINGS: Obesity is associated with a lower rate of referral and waitlisting, and lower likelihood of kidney transplantation. Despite increased risk for early surgical complications and delayed graft function, experience from multiple centers demonstrate a clear survival benefit of transplantation over dialysis in most obese patients, and comparable graft and patient survival rates to nonobese recipients. SUMMARY: Data suggest that long-term transplant outcomes among obese recipients are similar to those among nonobese. Strategies to achieve pretransplant weight reduction and minimally invasive surgical techniques may further improve results of kidney transplantation in obese recipients.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".