MétaCan
Menu
Back to cohort
Record W2763208908 · doi:10.1002/mus.25982

Effect of very old age on anconeus motor unit loss and compensatory remodelling

2017· article· en· W2763208908 on OpenAlexafffund
Kevin J. Gilmore, Eric A. Kirk, Timothy J. Doherty, Charles L. Rice

Bibliographic record

VenueMuscle & Nerve · 2017
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsReinnervationMotor unitElectromyographyMedicinePhysical medicine and rehabilitationAnatomyNeuroscienceInternal medicineCardiologyPsychology

Abstract

fetched live from OpenAlex

ABSTRACT Introduction: It is not known how the process of compensatory remodeling through collateral reinnervation continues into very old age (>80 years) or whether there is a limit to effective motor unit (MU) reinnervation. Therefore, we explore electrophysiological properties related to motor unit number estimates (MUNEs) in very old participants (79–90 years of age) compared with young controls (25–29 years of age). Methods: Decomposition‐enhanced spike‐triggered averaging was used to collect surface and intramuscular electromyography information from the anconeus to derive a MUNE. Results: Young participants had a MUNE of ∼38 and ∼25 at 30% and 50% root mean squared maximum voluntary contraction (RMS MVC ) with surface motor unit potentials (S‐MUPs) of ∼145 μV and 236 μV, respectively. Older participants had a MUNE of ∼23 and ∼16 at 30% and 50% RMS MVC with S‐MUPs of 168 μV and 232 μV, respectively. Discussion: In this muscle, an age limit to successful remodeling through collateral reinnervation, to compensate for the presumed ongoing losses of MUs, may have been surpassed. Muscle Nerve 57 : 659–663, 2018

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 score0.847
Threshold uncertainty score0.577

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)0.0000.000

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 head0.017
GPT teacher head0.240
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

Citations19
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueMuscle & NerveSame topicMuscle activation and electromyography studiesFrench-language works237,207