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Record W2766243478 · doi:10.1016/j.jalz.2017.06.2634

[IC‐03–04]: WHITE MATTER HYPERINTENSITIES IN GENETIC FRONTOTEMPORAL DEMENTIA: A GENFI STUDY

2017· article· en· W2766243478 on OpenAlexaff
Carole H. Sudre, Martina Bocchetta, David M. Cash, David L. Thomas, Ione Woollacott, Katrina M. Dick, John C. van Swieten, Barbara Borroni, Daniela Galimberti, Mario Masellis, Maria Carmela Tartaglia, James B. Rowe, Caroline Graff, Fabrizio Tagliavini, Giovanni B. Frisoni, Robert Laforce, Elizabeth Finger, Alexandre de Mendonça, Sandro Sorbi, Sébastien Ourselin, M. Jorge Cardoso, Jonathan D. Rohrer

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsWestern UniversityUniversité LavalUniversity Health NetworkSunnybrook Hospital
FundersMedical Research Council
KeywordsFrontotemporal dementiaC9orf72HyperintensityWhite matterFrontal lobeCardiologyPathologyMagnetic resonance imagingDementiaPsychologyMedicineInternal medicineNeuroscienceDiseaseRadiology

Abstract

fetched live from OpenAlex

Around a third of frontotemporal dementia (FTD) is caused by mutations in three main genes: progranulin (GRN), microtubule associated protein tau (MAPT) and chromosome 9 open reading frame 72 (C9orf72). Pathophysiological processes induced by these mutations may differ and some FTD subtypes have been associated with damage to the white matter (WM). This damage is visible as hyperintense signal on T2-weighted magnetic resonance (MR) imaging. The Genetic FTD Initiative (GENFI) is a longitudinal cohort study aimed at furthering understanding of disease in individuals with these three mutations. T1 and T2-weighted MR sequences were acquired for 180 subjects within the GENFI cohort, divided into 76 non-carriers, 61 presymptomatic mutation carriers (25 GRN, 8 MAPT and 28 C9orf72) and 43 symptomatic carriers (7 GRN, 13 MAPT and 23 C9orf72). WM hyperintensities (WMH) were automatically segmented using an algorithm based on outlier modelling in a multivariate Gaussian mixture model. Location in the WM was coded according to 1) a relative distance between ventricles and the cortex, divided into four equidistant layers (1 layer periventricular, 4 layer juxtacortical), and 2) to the closest cortical lobe. WMH in the basal ganglia were also investigated. Infratentorial regions were excluded from the analysis. Log-transformed WMH volumes were adjusted for age, gender, total intracranial volume, scanner type and years before expected onset. Symptomatic GRN carriers had significantly more WMH than all other groups, but no differences could be detected between other subgroups (table 1). Symptomatic GRN carriers appeared to have a larger volume of WMH in the frontal and occipital regions compared with other symptomatic groups and presymptomatic GRN cases. Differences were most noticeable in periventricular layers, with higher WMH volumes in GRN cases compared with other groups. WMH patterns differ across FTD genetic subtypes, with symptomatic GRN carriers displaying particularly predominant fronto-occipital periventricular WMH. Future research should explore the pathophysiological mechanisms of WMH within genetic FTD. Effect sizes of observed differences between groups at local (layers and lobes) and global scales; PS - Presymptomatic; S - Symptomatic.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.053
GPT teacher head0.317
Teacher spread0.264 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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