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Record W2050175727 · doi:10.1177/1352458507088106

Atrophy in white matter fiber tracts in multiple sclerosis is not dependent on tract length or local white matter lesions

2008· article· en· W2050175727 on OpenAlexaff
I.B. Kezele, Douglas L. Arnold, D. Louis Collins

Bibliographic record

VenueMultiple Sclerosis Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute on Aging
KeywordsAtrophyWhite matterMultiple sclerosisPathologyLesionFiber tractCorticospinal tractMedicineExpanded Disability Status ScalePathogenesisMagnetic resonance imagingDiffusion MRIAnatomyRadiology

Abstract

fetched live from OpenAlex

The pathogenesis of tissue injury outside the white matter (WM) plaques of multiple sclerosis (MS) has not yet been clearly defined. To better understand the pathogenesis of this injury and the associated atrophy, we investigated volume loss over time in 20 WM fiber tracts. We defined two main aims: (1) to examine whether certain fiber tracts were more prone to atrophy, and to test the possible relation of tract atrophy to tract length and selected MS-specific variables; and (2) to investigate the possible relation of atrophy to lesion load (whole brain and in the specific tract). Local volume change was assessed between two distant time points for each MS patient studied. Fiber tracts were segmented automatically using a tractography-based atlas. Results demonstrate volume loss in all fiber tracts. The uncinate fasciculus and anterior-thalamic radiation had the greatest yearly percentage atrophy. Disease type, duration, median expanded disability status scale, total lesion load, and gender exhibited significant effects on atrophy in at least one tract. Together, these data are more consistent with a pathogenesis for the degeneration related to diffuse inflammation rather than the secondary effects of focal lesions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0100.004

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 head0.154
GPT teacher head0.302
Teacher spread0.147 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2008
Admission routes1
Has abstractyes

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