Challenges in Diagnosis, Treatment and Follow-up of Patients Presenting with Central Nervous System Infections in a Resource-Limited Setting
Bibliographic record
Abstract
Central Nervous System (CNS) infections are associated with significant mortality and morbidity. Accurate diagnosis is necessary for prompt treatment and increased chances of survival. However, there are many challenges to correct diagnoses in resource-limited settings, including the HIV epidemic, late presentation of symptomatic individuals, limited availability of laboratory diagnostic tests as well as treatment, and inadequate access to funds accompanied by lack of financial support from developed countries. This article presents case reports of patients admitted to the Mulago Hospital in Kampala, Uganda that exemplify challenging diagnoses of tuberculous meningitis (TBM), cryptococcal meningitis (CM), toxoplasmosis, and primary CNS lymphoma (PCNSL). Also included is a literature review of the pathology, diagnosis, and treatment of TBM, CM, toxoplasmosis, and PCNSL in immunocompromised patients.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".