Central Nervous System Tuberculosis
Bibliographic record
Abstract
Tuberculosis (TB) has shown a resurgence in nonendemic populations in recent years and accounts for 8 million deaths annually in the world. Central nervous system involvement is one of the most serious forms of this infection, acting as a prominent cause of morbidity and mortality in developing countries. The rising number of cases in developed countries is mostly attributed to factors such as the pandemic of acquired immunodeficiency syndrome and increased migration in a globalized world. Mycobacterium TB is responsible for almost all cases of tubercular infection in the central nervous system. It can manifest in a variety of forms as tuberculous meningitis, tuberculoma, and tubercular abscess. Spinal infection may result in spondylitis, arachnoiditis, and/or focal intramedullary tuberculomas. Timely diagnosis of central nervous system TB is paramount for the early institution of appropriate therapy, because delayed treatment is associated with severe morbidity and mortality. It is therefore important that physicians and radiologists understand the characteristic patterns, distribution, and imaging manifestations of TB in the central nervous system. Magnetic resonance imaging is considered the imaging modality of choice for the study of patients with suspected TB. Advanced imaging techniques including magnetic resonance perfusion and diffusion tensor imaging may be of value in the objective assessment of therapy and to guide the physician in the modulation of therapy in these 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".