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Record W2047054588 · doi:10.1159/000144028

Magnetic Resonance Imaging in Frontotemporal Dementia Shows Subcortical Atrophy

2008· article· en· W2047054588 on OpenAlexaff
Tiffany W. Chow, Aaron Izenberg, Malcolm A. Binns, Morris Freedman, Donald T. Stuss, Chris J.M. Scott, Joel Ramirez, Sandra E. Black

Bibliographic record

VenueDementia and Geriatric Cognitive Disorders · 2008
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of TorontoBaycrest Hospital
FundersNational Institute on Aging
KeywordsAtrophyFrontotemporal dementiaPsychologyMagnetic resonance imagingPrimary progressive aphasiaNeuroscienceAphasiaDementiaPathologyMedicineDiseaseRadiology

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: The clinical syndrome of the frontotemporal dementias (FTD) overlaps with frontal-subcortical circuit syndromes. We explored the extent to which subcortical atrophy on structural magnetic resonance imaging may indicate a subcortical contribution to the progression of FTD. METHODS: This cross-sectional case-control study compared striatal and thalamic gray matter volumes and functional levels from 30 FTD cases and 30 age- and gender-matched controls. RESULTS: The FTD group had significantly more atrophy in all gray matter subcortical regions, correlating with ipsilateral frontocortical atrophy. Subcortical atrophy was also associated with functional disability. Subcortical asymmetry was most marked in subjects with primary progressive aphasia. CONCLUSION: Subcortical gray matter atrophy may contribute as significantly to symptoms of FTD as cortical atrophy.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.258
Teacher spread0.249 · 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

Citations41
Published2008
Admission routes1
Has abstractyes

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