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Record W2086755478 · doi:10.1002/mus.23720

7‐T MRI of the spinal cord can detect lateral corticospinal tract abnormality in amyotrophic lateral sclerosis

2012· article· en· W2086755478 on OpenAlexaff
Julien Cohen‐Adad, Wei Zhao, Boris Keil, Eva‐Maria Ratai, Christina Triantafyllou, Robert Lawson, Christina Dheel, Lawrence L. Wald, Bruce R. Rosen, Merit Cudkowicz, Nazem Atassi

Bibliographic record

VenueMuscle & Nerve · 2012
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsPolytechnique Montréal
FundersNational Center for Research Resources
KeywordsAmyotrophic lateral sclerosisMedicineSpinal cordCorticospinal tractMagnetic resonance imagingPyramidal tractsMultiple sclerosisCentral nervous system diseaseHyperintensityAbnormalityAnatomyPathologyRadiologyDiffusion MRIDiseaseSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder affecting lower and upper motor neurons. Degeneration of the lateral corticospinal tract (CST) is a key finding in ALS cervical spinal cord autopsies. We hypothesized that in vivo ultra-high-field MRI of the cervical spinal cord can detect abnormality in the CST. METHODS: A patient with ALS (disease duration 23 months) and a healthy control were scanned at 7-T MRI using a 19-channel coil. Multi-echo T2*-weighted imaging was performed in the spinal cord, covering C2-C6. Cross-sectional resolution was 0.37 × 0.37 mm(2). RESULTS: We detected clear signal hyperintensity in both segments of the lateral CST in the ALS patient, which was significant when compared with the normal control subject (P < 10(-7)). CONCLUSION: We believe there are potential benefits of 7-T MRI for increased sensitivity and spatial accuracy in characterizing pathology in the spinal cord.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.056
GPT teacher head0.308
Teacher spread0.253 · 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
Published2012
Admission routes1
Has abstractyes

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