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Record W4324030973 · doi:10.1016/j.seizure.2023.03.006

Seizures and epilepsy associated with central nervous system tuberculosis

2023· review· en· W4324030973 on OpenAlexaff
Ana Pilar Ramos, Jorge G. Burneo

Bibliographic record

VenueSeizure · 2023
Typereview
Languageen
FieldMedicine
TopicInfectious Diseases and Tuberculosis
Canadian institutionsWestern University
Fundersnot available
KeywordsEpilepsyCentral nervous systemTuberculosisMedicineNeurosciencePsychologyPathologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Central nervous system (CNS) tuberculosis is a life-threatening condition that usually presents with seizures, particularly in children and HIV-infected patients. Tuberculous meningitis (TBM) and tuberculomas are the two forms of CNS tuberculosis that can present with seizures. Seizures usually resolve after successful treatment of the underlying infection. However, the success of the treatment is usually based on an early diagnosis. Delay in the treatment of CNS tuberculosis increases the risk of its associated complications, such as stroke. This would lead to the development of epilepsy. Early seizures may be related to meningeal irritation and cerebral edema, whereas late seizures are often associated with structural brain lesions that generally require more advanced and prolonged treatment. Risk factors associated with the development of epilepsy include young age, refractory seizures, tuberculoma, cortical involvement, epileptiform discharges, and residual lesions. Treatment of CNS tuberculosis is based on early initiation of appropriate anti-tuberculous drugs, antiseizure medications, and correction of associated predisposing factors. Finally, further research into the mechanisms of seizures and the development of epilepsy in CNS tuberculosis could help improve management of these conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.027
GPT teacher head0.293
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2023
Admission routes1
Has abstractyes

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