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Record W2168337823 · doi:10.1186/1743-0003-7-39

Assessing motor deficits in compressive neuropathy using quantitative electromyography

2010· article· en· W2168337823 on OpenAlexaff
Joseph Y. Nashed, Andrew Hamilton-Wright, Daniel W. Stashuk, Matthew Faris, Linda McLean

Bibliographic record

VenueJournal of NeuroEngineering and Rehabilitation · 2010
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsUniversity of WaterlooQueen's University
Fundersnot available
KeywordsElectromyographyMotor unitReinnervationCarpal tunnel syndromeMedicinePhysical medicine and rehabilitationAnatomySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Studying the changes that occur in motor unit potential trains (MUPTs) may provide insight into the extent of motor unit loss and neural re-organization resulting from nerve compression injury. The purpose of this study was to determine the feasibility of using decomposition-based quantitative electromyography (DQEMG) to study the pathophysiological changes associated with compression neuropathy. METHODS: The model used to examine compression neuropathy was carpal tunnel syndrome (CTS) due to its high prevalence and ease of diagnosis. Surface and concentric needle electromyography data were acquired simultaneously from the abductor pollicis brevis muscle in six individuals with severe CTS, eight individuals with mild CTS and nine healthy control subjects. DQEMG was used to detect intramuscular MUPTs during constant-intensity contractions and to estimate parameters associated with the surface- and needle-detected motor unit potentials (SMUPs and MUPs, respectively). MUP morphology and stability, SMUP morphology and motor unit number estimates (MUNEs) were compared among the groups using Kruskal-Wallis tests. RESULTS: The severe CTS group had larger amplitude and longer duration MUPs and smaller MUNEs than the mild CTS and control groups, suggesting that the individuals with severe CTS had motor unit loss with subsequent collateral reinnervation, and that DQEMG using a constant-intensity protocol was sensitive to these changes. SMUP morphology and MUP complexity and stability did not significantly differ among the groups. CONCLUSIONS: These results provide evidence that MUP amplitude parameters and MUNEs obtained using DQEMG, may be a valuable tool to investigate pathophysiological changes in muscles affected by compressive motor neuropathy to augment information obtained from nerve conduction studies. Although there were trends in many of these measures, in this study, MUP complexity and stability and SMUP parameters were, of limited value.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.297
Teacher spread0.284 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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