Integrating APOL1 Gene Variants Into Renal Transplantation: Considerations Arising From the American Society of Transplantation Expert Conference
Bibliographic record
Abstract
Thirteen percent of individuals of African ancestry express two variant copies of the gene encoding apolipoprotein 1 (APOL1) that has been associated with an increased risk of end-stage renal disease (ESRD) in the general population. Limited studies suggest that the survival of transplanted kidneys from donors expressing two APOL1 risk alleles is inferior to that of kidneys from donors with zero or one risk allele. In living kidney donation, two case reports describe donors expressing two APOL1 risk alleles who developed ESRD. Given the potential impact of APOL1 variants on the utility and safety of kidney transplantation and living kidney donation, the American Society of Transplantation convened a meeting with the goals of summarizing the current state of knowledge with respect to transplantation and APOL1, identifying knowledge gaps and studies to address these gaps, and considering approaches to integrating APOL1 into clinical practice. The authors recognize that current data are not sufficient to support traditional evidence-based guidelines but also recognize that it may require several years to generate the necessary data. Thus, approaches as to how APOL1 might currently be integrated into the clinical decision-making process were considered. This report summarizes the group's deliberations.
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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 | 0.081 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.028 | 0.037 |
| Insufficient payload (model declined to judge) | 0.003 | 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".