Relative Contribution of Pharmacokinetics and Immune Signatures to Clinical Outcomes in Patients With HIV-associated Cryptococcal Meningitis
Bibliographic record
Abstract
Background: Host immune responses to HIV-associated cryptococcal meningitis are critical in disease outcome. Their interaction with antifungal drug exposure is poorly understood. This study explored associations between immune biomarkers, antifungal drug exposure, and clinical outcomes in HIV-associated cryptococcal meningitis. Methods: We analyzed serial plasma and cerebrospinal fluid immune biomarkers from 64 participants recruited from the AMBITION-cm trial. We estimated individual-level exposure to amphotericin B, flucytosine, and fluconazole. Associations between immune biomarkers, pharmacokinetic parameters, and clinical outcomes were evaluated. Results: An inflammatory cerebrospinal fluid response, characterized by coordination between tumor necrosis factor-α, granulocyte colony-stimulating factor, and interleukin-7 signaling, was linked to low fungal burden, low intracranial pressure, and survival. However, the value of specific immune biomarkers did not predict EFA or mortality. Exposure to amphotericin B was significantly associated with EFA. Conclusions: Favorable clinical outcomes from HIV-associated cryptococcal meningitis are associated with coordinated inflammatory and cytotoxic responses in the central nervous system. Antifungal drug exposure was the dominant predictor of EFA.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".