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Record W1981932028 · doi:10.1002/jmri.23504

Imaging of prion diseases

2012· review· en· W1981932028 on OpenAlexaff
Laurent Létourneau‐Guillon, Ryan Wada, Walter Kucharczyk

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPrion Diseases and Protein Misfolding
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsNeuroimagingDiffusion MRIPrion proteinImaging geneticsPrion ProteinsNeuroscienceDiffusion imagingMedicinePathologicalPathologyDiseaseMagnetic resonance imagingBiologyRadiology

Abstract

fetched live from OpenAlex

Prion diseases are caused by self-replicating proteins that induce lethal neurodegenerative disorders. In the last decade, the understanding of the different clinical, pathological, and neuroimaging phenotypes of this group of disorders has evolved paralleling the advances in prion molecular biology. From an imaging standpoint, the implementation of diffusion-weighted imaging in routine practice has markedly facilitated the detection of prion diseases, especially Creutzfeldt-Jakob. Less frequent prion-related disorders, including genetic diseases, may also benefit from progresses in the field of quantitative diffusion-weighted imaging, MR spectroscopy or molecular imaging. Herein, we present a review of the neuroimaging features of the prion disorders known to affect humans emphasizing the important contribution of MRI in the diagnosis of this group of disorders.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.299
Teacher spread0.283 · 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 teacher head, not a consensus.

Study designOther design
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

Citations36
Published2012
Admission routes1
Has abstractyes

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