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Record W2902410328 · doi:10.1097/rmr.0000000000000187

Imaging of Childhood Inflammatory Brain Diseases

2018· review· en· W2902410328 on OpenAlexaff
Sniya Sudhakar, Karthik Muthusamy, Manohar Shroff

Bibliographic record

VenueTopics in Magnetic Resonance Imaging · 2018
Typereview
Languageen
FieldMedicine
TopicAutoimmune Neurological Disorders and Treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsPathognomonicImmune systemMedicineAutoimmunityMagnetic resonance imagingInflammationCentral nervous systemNeuroimmunologyBiomarkerNeuroscienceImmunologyPathologyDiseaseBiologyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Brain has been considered as an immune-privileged site for centuries owing to the presence of blood-brain barrier, absent lymphatic drainage, and antigen-presenting cells. However, the present prevailing concept is of immune surveillance where brain is continuously surveyed by immune cells. However, the presence of immune cells in central nervous system (CNS) brings the risk of inflammation and autoimmunity involving both T and B cell mediated pathways. These mechanisms form the underlying pathology in a wide spectrum of pediatric CNS diseases manifesting as acquired neurological deficits. Overlapping, heterogenous, and ambiguous clinical features often delays the diagnosis. Although not always pathognomonic, magnetic resonance imaging can be an important biomarker leading to early diagnosis, prognostication, and systematic follow-up pf these diseases. This review describes the spectrum of different pediatric inflammatory disorders and their pertinent imaging features illustrated with clinical examples.

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
Bibliometrics0.0030.002
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.299
Teacher spread0.284 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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