Utility of Cerebrospinal Fluid Protein Levels as a Potential Predictive Biomarker of Disease Severity in HIV-Associated Cryptococcal Meningitis
Bibliographic record
Abstract
Background: Cerebrospinal fluid (CSF) protein levels exhibit high variability in HIV-associated cryptococcal meningitis from being normal to markedly elevated. However, the clinical implications of CSF protein levels in cryptococcal meningitis remain unclear. Methods: We analysed data from 890 adults with HIV-associated cryptococcal meningitis randomized into two clinical trials in Uganda between 2015 and 2021. CSF protein was grouped into ≥100 mg/dL (n=249) and <100 mg/dL (n=641). We described baseline clinical variables and mortality by CSF protein levels. Results: Approximately one-third of individuals had a baseline CSF protein ≥100 mg/dL. Those with CSF protein ≥100 mg/dL were more likely to present with Glasgow coma scale scores <15 (P<0.01), self-reported seizures at baseline (P=0.02), higher CD4 T-cells (p<0.001), and higher CSF white cells (p<0.001). Moreover, those with a baseline CSF protein ≥100 mg/dL also had a lower baseline CSF fungal burden (p<0.001) and a higher percentage of sterile CSF cultures at day 14 (p=0.02). Individuals with CSF protein ≥100 mg/dL demonstrated a more pronounced immune response consisting of upregulation of immune effector molecules pro-inflammatory cytokines, type-1 T-helper cell cytokines, type-3 chemokines, and immune-exhaustion marker (p<0.05). 18-week mortality risk in individuals with a CSF protein <100 mg/dL was 34% higher, (unadjusted Hazard Ratio 1.34; 95% CI, 1.05 to 1.70; p=0.02) than those with ≥100 mg/dL. Conclusion: In cryptococcal meningitis, individuals with CSF protein ≥100 mg/dL more frequently presented with seizures, altered mental status, immune activation, and favourable fungal outcomes. Baseline CSF protein levels may serve as a surrogate marker of immune activation and prognosis.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".