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Record W2322148656 · doi:10.1177/1352458515604382

Multiple sclerosis masquerading as Alzheimer-type dementia: Clinical, radiological and pathological findings

2015· article· en· W2322148656 on OpenAlexaff
W. Oliver Tobin, BF Popescu, Val J. Lowe, Istvan Pirko, J. E. Parisi, Kejal Kantarci, J. F. Fields, MB Bruns, B. F. Boeve, CF Lucchinetti

Bibliographic record

VenueMultiple Sclerosis Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Saskatchewan
FundersNational Institute of Neurological Disorders and StrokeNational Institute on Aging
KeywordsAutopsyMedicinePathologyDementiaMultiple sclerosisPathologicalMagnetic resonance imagingWhite matterMyelinNeuropathologyDegenerative diseaseAlzheimer's diseaseDiseaseRadiologyCentral nervous systemInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: We report a comprehensive clinical, radiological, neuropsychometric and pathological evaluation of a woman with a clinical diagnosis of AD dementia (ADem), but whose autopsy demonstrated widespread demyelination, without Alzheimer disease (AD) pathology. METHODS AND RESULTS: Initial neuropsychometric evaluation suggested amnestic mild cognitive impairment (aMCI). Serial magnetic resonance images (MRI) images demonstrated the rate of increase in her ventricular volume was comparable to that of 46 subjects with aMCI who progressed to ADem, without accumulating white matter disease. Myelin immunohistochemistry at autopsy demonstrated extensive cortical subpial demyelination. Subpial lesions involved the upper cortical layers, and often extended through the entire width of the cortex. CONCLUSIONS: Multiple sclerosis (MS) can cause severe cortical dysfunction and mimic ADem. Cortical demyelination is not well detected by standard imaging modalities and may not be detected on autopsy without myelin immunohistochemistry.

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)0.005
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.425
GPT teacher head0.388
Teacher spread0.037 · 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 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

Citations18
Published2015
Admission routes1
Has abstractyes

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