Metabolic, cardiovascular, and substance use evaluation of living kidney donor candidates: US practices in 2017
Bibliographic record
Abstract
We surveyed US transplant centers to assess practices regarding the evaluation and selection of living kidney donors based on metabolic, cardiovascular, and substance use risk factors. Our companion article describes renal aspects of the evaluation. Response rate was 31%. Compared with 2005, programs have become more accepting of hypertensive candidates: 65% in 2017% vs 41% in 2005 consider candidates with hypertension well controlled with 1 medication. One notable exception is black hypertensive candidates, who are frequently excluded regardless of severity. The most common body mass index (BMI) cutoff remains 35 kg/m2, and fewer programs now consider candidates with BMI >40 kg/m2. A 2-hour oral glucose tolerance test of ≥140 mg/dL remains the most common criterion for exclusion of prediabetic candidates. One quarter to one third of programs exclude based on isolated cardiac abnormalities, such as mild aortic stenosis; a similar proportion consider these candidates only if older than 50 years. Cigarette or marijuana smoking are infrequently criteria for exclusion, although 45% and 37% programs, respectively, require cessation 4 weeks prior to surgery. In addition to providing an overview of current practices in living kidney donor evaluation, our study highlights the importance of research evaluating outcomes with various comorbidities to guide practice.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".