The medical management of central nervous system infections in Uganda and the potential impact of an algorithm-based approach to improve outcomes
Bibliographic record
Abstract
BACKGROUND: In sub-Saharan Africa, HIV has increased the spectrum of central nervous system (CNS) infections. The etiological diagnosis is often difficult. Mortality from CNS infections is higher in sub-Saharan Africa compared to Western countries. This study examines the medical management of CNS infections in Uganda. We also propose a clinical algorithm to manage CNS infections in an effective, systematic, and resource-efficient manner. METHODS: We prospectively followed 100 consecutive adult patients who were admitted to Mulago Hospital with a suspected diagnosis of a CNS infection without any active participation in their management. From the clinical and outcome data, we created an algorithm to manage CNS infections, which was appropriate for this resource-limited, high HIV prevalence setting. RESULTS: Only 32 patients had a laboratory confirmed diagnosis and 23 of these were diagnosed with cryptococcal meningitis. Overall mortality was 39%, and mortality trended upward when the diagnosis was delayed past 3 days. The initial diagnoses were made clinically without significant laboratory data in 92 of the 100 patients. Because HIV positive patients have a unique spectrum of CNS infections, we created an algorithm that identified HIV-positive patients and diagnosed those with cryptococcal meningitis. After cryptococcal infection was ruled out, previously published algorithms were used to assist in the early diagnosis and treatment of bacterial meningitis, tuberculous meningitis, and other common central nervous system infections. In retrospective comparison with current management, the CNS algorithm reduced overall time to diagnosis and initiate treatment of cryptococcal meningitis from 3.5 days to less than 1 day. CONCLUSIONS: CNS infections are complex and difficult to diagnose and treat in Uganda, and are associated with high in-hospital mortality. A clinical algorithm may significantly decrease the time to diagnose and treat CNS infections in a resource-limited setting.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".