Risk of Multiple Sclerosis in People Living with HIV: An International Cohort Study
Bibliographic record
Abstract
Objective: \nThere 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. \n// \nMethods: \nPopulation-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. \n// \nResults: \nThe 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). \n// \nInterpretation: \nThis 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".