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Record W2339425991 · doi:10.1177/1352458515624559

High-resolution myelin water imaging in post-mortem multiple sclerosis spinal cord: A case report

2016· article· en· W2339425991 on OpenAlexaff
Cornelia Laule, Andrew Yung, Vlady Pavolva, Barry Bohnet, Piotr Kozłowski, Stanley A. Hashimoto, Stephen Yip, David Li, GR Wayne Moore

Bibliographic record

VenueMultiple Sclerosis Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
Fundersnot available
KeywordsMultiple sclerosisSpinal cordMyelinMedicinePathologyMyelin sheathMagnetic resonance imagingCentral nervous system diseaseHigh resolutionNeuroscienceCentral nervous systemAnatomyRadiologySurgeryBiologyGeologyInternal medicineImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Loss of myelin in the spinal cord in multiple sclerosis (MS) is likely an important, and early, contributor to atrophy and associated disability. In vivo measurement of myelin is possible using myelin water fraction (MWF) imaging, but MWF has never been assessed in MS along the entire length of the spinal cord in vivo or in post-mortem tissue. OBJECTIVE: To assess the feasibility of measuring the distribution of MWF along the entire length of the spinal cord in post-mortem MS tissue using high-field MRI. METHODS: One formalin-fixed spinal cord from a female with secondary progressive MS (age: 78 years, disease duration: 25 years) was cut into 104 5-mm-thick cross sections along the entire length of the spinal cord from the cervico-medullary junction to the conus medullaris and imaged using a 64 echo T2 relaxation experiment at 7T. RESULTS: Myelin water maps showed cord anatomy in superb detail, white matter demonstrating a higher MWF than the grey matter. Anatomical variation in myelin distribution along cervical, thoracic and lumbar regions was observed. Lesions demonstrated myelin loss. CONCLUSION: Post-mortem myelin water imaging of formalin-fixed MS spinal cord is feasible.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.099
GPT teacher head0.313
Teacher spread0.214 · 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

Citations37
Published2016
Admission routes1
Has abstractyes

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