Bibliographic record
Abstract
It is well recognized that African Americans of sub-Saharan African ancestry have nearly a 4-fold increased prevalence of endstage kidney disease (ESKD) over European Americans1,2,3,4. In 2008, two coding alleles in the apolipoprotein L1 gene ( APOL1 ), G1 and G2, were discovered to account for the majority of excess risk in progressive nondiabetic kidney disease in African Americans1,2,3,5. The several forms of APOL-1 –associated kidney disease include focal segmental glomerulosclerosis (FSGS), human immunodeficiency virus–associated nephropathy (HIVAN), hypertension-attributed ESKD, and sickle cell nephropathy5,6,7,8. There is a strong biallelic effect observed such that a high-risk genotype defined as the presence of 2 APOL1 risk alleles confers the strongest risk for HIVAN in the United States7 with an OR of 29 (95% CI 13–68), and an OR of 89 (95% CI 18–912) in South Africa9. This observation of stronger adverse kidney outcomes associated with APOL1 risk alleles was the rationale for a report by Vajgel, et al 10 that appears in this issue of The Journal . Their study involved genotyping APOL1 G1 and G2 risk alleles in 201 nonwhite Brazilian patients with lupus nephritis (LN) and 222 healthy blood donors. Because of the low APOL1 biallelic frequency in LN cases (2%), the authors had limited power to test biallelic effects and instead examined monoallelic APOL1 risk allele effect on LN outcomes. The authors observed … Address correspondence to Dr. L.T. Hiraki, PGCRL, 686 Bay St., Toronto, Ontario M5G 0A4, Canada. E-mail: Linda.hiraki{at}sickkids.ca.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".