Risk of Multiple Sclerosis in People Living with <scp>HIV</scp>: An International Cohort Study
Bibliographic record
Abstract
OBJECTIVE: There has been interest in a possible negative association between HIV and multiple sclerosis (MS). We aimed to compare the risk of MS in a cohort of individuals living with HIV to that in the general population. METHODS: Population-based health data were accessed for 2 cohorts of HIV-positive persons from Sweden and British Columbia, Canada. Incident MS was identified using MS registries or a validated algorithm applied to administrative data. Individuals with HIV were followed from 1 year after the first clinical evidence of HIV or the first date of complete administrative health data (Canada = April 1, 1992 and Sweden = January 1, 2001) until the earliest of incident MS, emigration, death, or study end (Canada = March 31, 2020 and Sweden = December 31, 2018). The observed MS incidence rate in the HIV-positive cohort was compared to the expected age-, sex-, calendar year-, income-specific, and region of birth-specific rates in a randomly selected sample of >20% of each general population. The standardized incidence ratio (SIR) for MS following the first antiretroviral therapy exposure ("ART-exposed") was also calculated. RESULTS: The combined Sweden-Canada cohort included 29,163 (75% men) HIV-positive persons. During 242,248 person-years of follow-up, 14 incident MS cases were observed in the HIV-positive cohort, whereas 26.19 cases were expected. The SIR for MS in the HIV-positive population was 0.53 (95% confidence interval [CI] = 0.32-0.90). The SIR for MS following the first ART exposure was 0.55 (95% CI = 0.31-0.96). INTERPRETATION: This international population-based study demonstrated a lower risk of MS among HIV-positive individuals, and HIV-positive ART-exposed individuals. These findings provide support for further exploration into the relationship among HIV, ART, and MS. ANN NEUROL 2024;95:487-494.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".