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Record W3216911663 · doi:10.4081/ejtm.2021.10200

Length-dependent changes of lower limb muscle morphology in Chronic Inflammatory Demyelinating Polyneuropathy assessed with magnetic resonance imaging

2021· article· en· W3216911663 on OpenAlexafffund
Jacob Fanous, Alexander M. Zero, Kevin J. Gilmore, Timothy J. Doherty, Charles L. Rice

Bibliographic record

VenueEuropean Journal of Translational Myology · 2021
Typearticle
Languageen
FieldMedicine
TopicPeripheral Neuropathies and Disorders
Canadian institutionsUniversity of TorontoWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMedicineMagnetic resonance imagingThighChronic inflammatory demyelinating polyneuropathyHamstringTriceps surae muscleAnatomyPhysical medicine and rehabilitationInternal medicineRadiology

Abstract

fetched live from OpenAlex

The objective of the present study was to assess muscle quantity of the thigh and leg in patients with chronic inflammatory demyelinating polyneuropathy (CIDP) compared to age and sex matched controls in exploring length-dependent changes of innervated muscles. In five people with CIDP and seven controls, magnetic resonance imaging was used to assess muscle morphology of the four parts of the quadriceps and medial hamstring muscles. Findings were compared to the triceps surae from a subset of participants. The CIDP group had less contractile tissue in the quadriceps (11.5%, P<0.05), hamstrings (15.6%, P<0.05) and triceps surae (35.9%, P<0.05) compared to controls. Additionally, CIDP had less contractile tissue (18.7%) in the triceps surae compared to the hamstrings (P<0.05). Muscle quantity in the quadriceps and hamstrings in CIDP was less than controls, but differences were greater for the distal triceps surae. These findings support a length-dependent affect of CIDP on limb musculature composition.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.560

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 head0.011
GPT teacher head0.235
Teacher spread0.223 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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