Types of Stroke Among People Living With HIV in the United States
Bibliographic record
Abstract
BACKGROUND: Most studies of stroke in people living with HIV (PLWH) do not use verified stroke diagnoses, are small, and/or do not differentiate stroke types and subtypes. SETTING: CNICS, a U.S. multisite clinical cohort of PLWH in care. METHODS: We implemented a centralized adjudication stroke protocol to identify stroke type, subtype, and precipitating conditions identified as direct causes including infection and illicit drug use in a large diverse HIV cohort. RESULTS: Among 26,514 PLWH, there were 401 strokes, 75% of which were ischemic. Precipitating factors such as sepsis or same-day cocaine use were identified in 40% of ischemic strokes. Those with precipitating factors were younger, had more severe HIV disease, and fewer traditional stroke risk factors such as diabetes and hypertension. Ischemic stroke subtypes included cardioembolic (20%), large vessel atherosclerosis (13%), and small vessel (24%) ischemic strokes. Individuals with small vessel strokes were older, were more likely to have a higher current CD4 cell count than those with cardioembolic strokes and had the highest mean blood pressure of the ischemic stroke subtypes. CONCLUSION: Ischemic stroke, particularly small vessel and cardioembolic subtypes, were the most common strokes among PLWH. Traditional and HIV-related risk factors differed by stroke type/subtype. Precipitating factors including infections and drug use were common. These results suggest that there may be different biological phenomena occurring among PLWH and that understanding HIV-related and traditional risk factors and in particular precipitating factors for each type/subtype may be key to understanding, and therefore preventing, strokes among PLWH.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".