Tunnel sign on magnetic resonance imaging in neuromelioidosis: A systematic literature review
Bibliographic record
Abstract
Background Neuromelioidosis can present with abscesses, meningitis, or encephalomyelitis, but can be missed on blood culture. Linear enhancement of the corticospinal tract (white matter motor pathway) on magnetic resonance imaging (MRI) in the form of a ‘tunnel sign’ is an essential clue for early diagnosis of neuromelioidosis. This systematic review (SR) explores the clinical profile and outcomes of neuromelioidosis patients with tunnel signs. Methods An SR was conducted to look for articles reporting individual details of neuromelioidosis patients with tunnel signs (reported or present on published images) on MRI. This review followed PRISMA guidelines and was prospectively registered with PROSPERO (CRD42024597199). After title-abstract and full-text screening, clinical profile and outcome data were extracted and analysed. Results Thirty cases (22 articles) with tunnel signs on MRI were included after screening 2985 articles. The traditional risk factors (diabetes mellitus, alcohol intake, steroids, etc.) for melioidosis were present in only 23% (5/22) of patients. Limb weakness (89%, 24/27) and cranial nerve involvement (46%, 11/24) were commonly seen at presentation. Blood and cerebrospinal fluid (CSF) cultures for B.pseudomallei were only positive in 15% (2/13) and 22% (4/18). Due to low rates of clinical suspicion of neuromelioidosis (25%, 6/24), empirical steroids and inappropriate antimicrobials were given in 47% (8/17) and 65% (9/17) of patients, respectively. A total of 30% (n=9) of the patients died. Conclusion In melioidosis-endemic areas with access to MRI, recognising the link between the presence of a tunnel sign and neuromelioidosis is crucial to initiate early adequate therapy.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".