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Record W2062776012 · doi:10.1002/mrm.21936

Myelin water measurement in the spinal cord

2009· article· en· W2062776012 on OpenAlexafffund
Evan Minty, Thorarin A. Bjarnason, Cornelia Laule, Alex L. MacKay

Bibliographic record

VenueMagnetic Resonance in Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Alberta
FundersMultiple Sclerosis SocietyNatural Sciences and Engineering Research Council of CanadaMultiple Sclerosis Society of Canada
KeywordsMultiple sclerosisMyelinSpinal cordMagnetic resonance imagingWhite matterLumbarContext (archaeology)T2 relaxationNuclear medicineMedicineNuclear magnetic resonanceBiomedical engineeringAnatomyCentral nervous systemNeuroscienceRadiologyBiologyPhysicsInternal medicine

Abstract

fetched live from OpenAlex

The desire to monitor the spatial-temporal characteristics of myelination in the spinal cord (SC), in the context of pathological change in demyelinating diseases or proposed neuroregenerative protocols, has led to an interest in noninvasive image-based myelin measurement methods. We present one strategy: a magnetic resonance-based measure that capitalizes on the characteristics of T(2) relaxation of water compartmentalized within tissue. In this study, 32-echo relaxation studies for measuring the myelin water fraction (MWF) were applied in healthy control SC in vivo using a sagittal inversion recovery multiecho sequence, and findings were supported with supplemental studies in bovine SC samples in vitro. Mean human MWF varied according the level of the SC examined: cervical, thoracic, and lumbar MWF was found to be 21.8 (SD=2.1)%, 24.3 (3.6)%, and 11.4 (6.4)%, respectively. Noteworthy reductions were observed in areas consistent with the expected locations of the cervical and lumbar enlargements. Average bovine MWF was 30.0 (2.7)% in white matter and 8.2 (0.4)% in gray matter. The potential applications of T(2) measurement in SC, both in characterizing disease processes like multiple sclerosis and in monitoring neuroregenerative therapies, should encourage future research in this area.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.119
GPT teacher head0.381
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations47
Published2009
Admission routes2
Has abstractyes

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