Neurologic infections in people with HIV: shifting epidemiological and clinical patterns
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to define the frequency, risk factors, and clinical outcomes of both AIDS-defining and non-AIDS-defining neurologic infections among people with HIV (PWH). DESIGN: We conducted a retrospective observational cohort study by linking the clinical database at the Southern Alberta HIV Clinic (SAC) with the regional hospital and microbiology databases to identify cases and the associated morbidity and mortality for these neurologic infections from 1995 to 2018. METHODS: Neurologic infections were categorized into AIDS-defining and non-AIDS defining. Annual incidence rates per 1000 person-years were calculated. Cox proportional hazards models estimated adjusted hazard ratios (aHR) and 95% confidence intervals of risk factors for neurologic infections in PWH and mortality outcomes. RESULTS: Among 2910 PWH contributing 24 237 years of follow-up, 133 (4.6%) neurologic infections were identified; 107 (80%) were AIDS-defining and 26 (20%) non-AIDS defining. While the incidence of AIDS-defining neurologic infections declined over time, no change was seen in incidence of non-AIDS defining infections. The risk of having any neurologic infection was greater among black PWH (aHR = 2.5 [1.6-4.0]) (vs. white PWH) and those with a CD4 + T-cell nadir of less than 200 cells/μl (aHR = 6.6 [4.0-11.1]) (vs. ≥200 cells/μl). More AIDS-defining neurologic infections occurred in PWH with lower CD4 + T-cell counts and higher HIV viral loads. PWH with any neurologic infections experienced more seizures, strokes, all-cause mortality (aHR = 2.2 [1.5-3.2] and HIV-related mortality (aHR = 6.4 [3.9-10.7] (vs. no neurologic infection). CONCLUSION: Both AIDS and non-AIDS defining neurologic infections continue to occur in PWH resulting in significant morbidity and mortality. Early diagnosis and initiation of ART remain crucial in preventing neurological infections in PWH.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.001 |
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".