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Record W2162198736 · doi:10.1212/wnl.0b013e31829bfd63

Magnetic resonance frequency shifts during acute MS lesion formation

2013· article· en· W2162198736 on OpenAlexfundno aff
Vanessa Wiggermann, Enedino Hernández Torres, Irene M. Vavasour, George R. Moore, Cornelia Laule, Alex L. MacKay, David K.B. Li, Anthony Traboulsee, Alexander Rauscher

Bibliographic record

VenueNeurology · 2013
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsLesionMagnetic resonance imagingWhite matterMultiple sclerosisMedicineGadoliniumNuclear medicineEdemaRadiologyPathologyNuclear magnetic resonanceInternal medicineChemistryPhysics

Abstract

fetched live from OpenAlex

OBJECTIVE: We investigated the evolution of new multiple sclerosis (MS) lesions over time using frequency shifts of the magnetic resonance (MR) signal. METHODS: Twenty patients with relapsing-remitting MS were serially scanned for 6 months at 1-month intervals. Maps of MR frequency shifts were acquired using susceptibility-weighted imaging. New lesions were identified by enhancement with gadolinium (Gd). RESULTS: Forty new lesions were identified as areas of signal increase on Gd-enhanced scans. Up to 3 months before lesion appearance, the frequency in areas of future Gd enhancement was not detectably different from the frequency in normal-appearing white matter. Rapid increase in MR frequency was observed between 1 month before and 1 month after Gd enhancement. Two months postenhancement and later, the frequency stabilized and remained at a constantly increased level. CONCLUSIONS: These findings suggest that an increase in MR frequency does not simply reflect blood-brain barrier disruption or edema; rather, it reflects a change of tissue architecture as a consequence of new lesion formation. The data demonstrate that the MR frequency of focal MS lesions is increased before the lesions appear on conventional MRI. Unlike many other advanced imaging techniques, the images for frequency mapping can be rapidly acquired at high spatial resolution and standardized on most clinical scanners.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.034
GPT teacher head0.291
Teacher spread0.257 · 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 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

Citations61
Published2013
Admission routes1
Has abstractyes

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