Comparative Effectiveness of Induction Therapy for Human Immunodeficiency Virus-Associated Cryptococcal Meningitis: A Network Meta-Analysis
Bibliographic record
Abstract
Background. Multiple international treatment guidelines recommend amphotericin-based combination regimens for induction therapy of cryptococcal meningitis. Yet, only 1 trial has reported a mortality benefit for combination amphotericin-flucytosine, and none have reported a mortality benefit for combination amphotericin-fluconazole. Methods. We conducted a Bayesian network meta-analysis to estimate the comparative effectiveness of recommended induction therapies for HIV-associated cryptococcal meningitis. We searched PubMed and Cochrane CENTRAL for clinical reports of induction therapy for HIV-associated cryptococcal meningitis. We extracted or calculated early (two-week) and late (six to 12-week) mortality by treatment arm for the following induction regimens: amphotericin B alone, amphotericin B + flucytosine, amphotericin B + triazoles, amphotericin B + flucytosine +triazoles, triazoles alone, triazoles + flucytosine, liposomal amphotericin B, and amphotericin B + other medicines. Results. In the overall sample (35 studies, n = 2483), we found no evidence of decreased mortality from addition of flucytosine or triazoles to amphotericin B, compared with amphotericin B alone. Although we did find a nonsignificant benefit for addition of flucytosine to amphotericin B in studies including participants with altered levels of consciousness, we did not identify a benefit for combination therapy in restricted analyses in either resource-rich or resource-limited settings, studies conducted before or after 2004, and studies restricted to a high dose of amphotericin B and fluconazole. Conclusions. Given considerations of drug availability and toxicity, there is an important need for additional data to clarify which populations are most likely to benefit from combination therapies for human immunodeficiency virus-associated cryptococcal meningitis.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.055 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".