Overweight Kidney Donors Gain Weight after Donation
Bibliographic record
Abstract
Introduction Living kidney donors remain at low risk of end stage kidney disease but obese and overweight donors' risk is increased. We aimed to describe the trajectory of weight changes pre/post donation to aid in donor risk assessment. Methods A single center retrospective chart review from 1/2009-12/2017 was performed. Demographics, height and weight measures from hospital and clinic visits were collected. Patients were categorized by BMI (kg/m2) at time of donation. Mean weight at time of initial assessment, kidney donation, 12 mos follow-up and last follow-up were calculated. Paired t-tests compared mean differences in weight at kidney donation relative to other time points. Results 195 donors were included for analysis. Mean (SD) age was 47±13 years and 73% were female. At donation, 2 patients (1.0%) had BMI <18.5 kg/m2 (underweight); 49 (25.1%) BMI 18.5-24.9 (healthy weight); 85 (43.6%) BMI 25-29.9 (overweight); 46 (23.6%) BMI 30-34.9 (Class 1 Obesity); 13 (6.7%) BMI 35-39.9 (Class 2 Obesity). Weight loss occurred prior to donation in the BMI 18.5-24.9 group and did not change post-donation. Significant weight gain occurred following donation in patients with BMI ≥ 25 (P<0.0001). Conclusions Despite KDIGO recommendations that obese and overweight patients pursue weight loss before donation and donors maintain a healthy weight after donation, this is not what occurred. Significant weight gain occurred in patients with BMI ≥ 25 at donation. This finding may influence donor risk assessment. Our results highlight the need for effective weight loss interventions both pre- and post-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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".