Association between ethnicity and human leukocyte antigen (HLA) alleles on late presentation to care and high rates of opportunistic infections in patients with HIV
Bibliographic record
Abstract
Within Manitoba, Aboriginal people make up 15% of the province’s population, but accounted for 53% of new human immunodeficiency virus (HIV) diagnoses in 2011. For over a decade, research has linked the human leukocyte antigen (HLA) class I alleles as having both protective and harmful effects in HIV disease progression. The abundance of HLA alleles that predispose to rapid disease progression, together with the rarity of protective HLA allele types, may be a contributing factor to a more rapid disease progression amongst individuals of Aboriginal ethnicity. We completed an epidemiological study on all HIV patients new to care in the Manitoba HIV Program in 2010, looking at markers of disease severity, such as CD4 cell count, rates of opportunistic infections (OI), and HLA type. In this cohort, the Aboriginal population was overrepresented, and presented with significantly more advanced HIV infection (lower CD4 counts, higher rates of OI), compared to patients from a Caucasian background. Our data supports previously identified associations between HLA type and disease progression, and demonstrates a difference in distribution of HLA type by ethnicity. Key words: Aboriginal, disease progression, ethnicity, HLA B27, HLA B35, HLA B51, HLA B5701, human immunodeficiency virus, human leukocyte antigen, opportunistic infection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".