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Record W4364351978 · doi:10.1002/epd2.20060

Neurocysticercosis and epilepsy: Imaging and clinical characteristics

2023· article· en· W4364351978 on OpenAlexaff
Ildefonso Rodríguez‐Leyva, Karla Cantú‐Flores, Arturo Domínguez‐Frausto, Anna Elisabetta Vaudano, John S. Archer, Boris C. Bernhardt, Lorenzo Caciagli, Fernando Cendes, Yotin Chinvarun, Paolo Federico, William D. Gaillard, Eliane Kobayashi, Godwin Ogbole, Stefan Rampp, Irène Wang, Shuang Wang, Luis Concha

Bibliographic record

VenueEpileptic Disorders · 2023
Typearticle
Languageen
FieldMedicine
TopicParasitic infections in humans and animals
Canadian institutionsUniversity of CalgaryHotchkiss Brain InstituteMcGill UniversityMontreal Neurological Institute and Hospital
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México
KeywordsNeurocysticercosisTaenia soliumNeuroimagingEpilepsyMedicineCysticercosisPediatricsPathologyPsychiatry

Abstract

fetched live from OpenAlex

The ILAE Neuroimaging Task Force aimed to publish educational case reports highlighting basic aspects related to neuroimaging in epilepsy consistent with the educational mission of the ILAE. Neurocysticercosis (NCC) is highly endemic in resource-limited countries and increasingly more often seen in non-endemic regions due to migration. Cysts with larva of the tapeworm Taenia solium lodge in the brain and cause several neurological conditions, of which seizures are the most common. There is great heterogeneity in the clinical presentation of neurocysticercosis because cysts vary in number, larval stage, and location among patients. We here present two illustrative cases with different clinical features to highlight the varying severity of symptoms secondary to this parasitic infestation. We also present several examples of imaging characteristics of the disease at various stages, which emphasize the central role of neuroimaging in the diagnosis of neurocysticercosis.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.336
Teacher spread0.316 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2023
Admission routes1
Has abstractyes

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