Movement Disorders in Central Nervous System Tuberculosis: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) affecting the central nervous system (CNS) can lead to a broad range of movement disorders, which are frequently overlooked in clinical settings. This review explores the various presentations, underlying mechanisms and patient outcomes related to these disorders. METHODS: We systematically reviewed published case reports, series and cohort studies that described patients with CNS TB who developed movement disorders. Extracted data included patient characteristics, type of CNS TB, imaging and CSF findings, types of movement disorders, treatments used and outcomes. RESULTS: A total of 61 patients with CNS TB and associated movement disorders were analyzed. The most common manifestations were ataxia, dystonia, chorea or hemiballismus and parkinsonism. Less frequent symptoms included opsoclonus-myoclonus, segmental myoclonus, tremors, cervical dystonia and stereotypy. TB meningitis was the predominant form, often accompanied by infarcts, hydrocephalus or tuberculomas. Proposed causes included vascular injury, inflammatory lesions, immune mechanisms and drug-related effects. All patients received anti-tuberculosis treatment, and nearly half required corticosteroids or surgical procedures. About 25 patients (41%) fully recovered, 17 (27.9%) had significant improvement, 13 (21.3%) showed partial improvement, 11 (18.0%) had ongoing problems and 1 (1.6%) died. Cohort studies also reveal that movement disorders - particularly tremors, dystonia, parkinsonism and ataxia - are frequent but underrecognized in CNS TB. These typically emerge early, often within three weeks, and are linked to lesions in the basal ganglia or thalamus. CONCLUSIONS: Movement disorders in CNS TB are more common than generally recognized. Prompt diagnosis through imaging and timely therapy can greatly improve neurological outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".