Influence of HLA-B*5703 and HLA-B*1403 on Susceptibility to Spondyloarthropathies in the Zambian Population
Bibliographic record
Abstract
OBJECTIVE: To analyze the distribution of HLA-B alleles and to investigate their contribution in the susceptibility to spondyloarthropathies (SpA) in a sample population from Zambia, in order to determine a relationship between some HLA-B alleles and development of ankylosing spondylitis (AS), reactive arthritis (ReA), or undifferentiated SpA (uSpA). METHODS: . We selected 72 patients with SpA and found that 46 had uSpA, 23 ReA, and 3 AS. We also selected 92 matched controls; 55 of these had human immunodeficiency virus type I (HIV-I) infection. RESULTS: We found a significant increase in the rate of uSpA and ReA with features of Reiter's syndrome (RS) in HIV-positive individuals who carried the HLA-B*5703 allele (pc < 0.0001 and pc < 0.001, respectively). Among the significant new findings identified were the presence of B*1403 in 2 of the 3 AS patients (pc < 0.05, OR 47), confirming previous data in the Togolese population. CONCLUSION: The presence of B*5703 and HIV infection may not affect susceptibility to AS and ReA, but they do show an important influence in uSpA and RS. Our findings confirm that HLA-B*1403 is the only factor to increase the risk of AS in a sub-Saharan African population, whereas HLA-B27 was virtually absent in patients with AS.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".