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Record W1528287032 · doi:10.1002/mus.24212

Motor unit number estimations are smaller in children with type 1 diabetes mellitus: A case–cohort study

2014· article· en· W1528287032 on OpenAlexaff
Cory Toth, Valerie Hebert, Claire Gougeon, Heidi Virtanen, Jean K. Mah, Danièle Pacaud

Bibliographic record

VenueMuscle & Nerve · 2014
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsAlberta Children's HospitalFoothills Medical CentreUniversity of Calgary
FundersJuvenile Diabetes Research Foundation International
KeywordsSubclinical infectionMedicineCohortElectrophysiologyDiabetes mellitusMotor unitInternal medicineDemographicsPediatricsEndocrinologyAnatomy

Abstract

fetched live from OpenAlex

INTRODUCTION: We studied the potential for motor unit number estimation (MUNE) to detect subclinical changes in motor unit numbers in children with type 1 diabetes mellitus (DM). METHODS: Blinded observers performed clinical assessment, electrophysiology, and multipoint MUNE of the extensor digitorum brevis muscle in children with DM for ≥ 5 years and age-matched healthy controls. RESULTS: For 51 DM subjects, the disease duration was 9.1 ± 2.6 years. Subjects with DM and healthy controls (n=21) had similar demographics. There were no clinical symptoms or signs of peripheral neuropathy in any subject, nor differences in standard electrophysiology between cohorts. Estimated motor unit numbers were decreased significantly in children with DM (224 ± 87 vs. 274 ± 101, P=0.036). CONCLUSION: Despite the absence of clinical or standard electrophysiological differences from normal control subjects, MUNE can detect a small significant difference in children with DM, suggesting that motor unit loss begins early and subclinically in the disease.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.217
Teacher spread0.207 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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