MétaCan
Menu
← Back to cohort
Record W7133035171

Investigating the Effect of Sensory Input on Muscle Function

2023· dissertation· W7133035171 on OpenAlexaff
Karlo Nesovic

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsToronto Rehabilitation Institute
Fundersnot available
KeywordsMotor unitSensory systemElectromyographyMotor unit recruitmentMuscle stiffnessNeuroplasticityBiomechanicsProprioceptionTibialis anterior muscleElectrophysiology
DOInot available

Abstract

fetched live from OpenAlex

Central sensitization (CSens) is a pain mechanism marked by hypersensitivity to stimuli. It is believed that neuroplastic changes caused by increased nociceptive input may allow sensory information to affect motor pathways. This study investigated the relationship between CSens and muscle function. Fifteen healthy participants underwent two interventions where capsaicin either induced CSens or was blocked with lidocaine. Three measurements were taken from the soleus and tibialis anterior muscles: shear wave elastography to monitor changes in muscle stiffness; surface electromyography to measure motor unit recruitment order, and F-wave amplitude and persistence to measure motor neuron excitability; torque recordings to determine force steadiness. An increase in the stiffness of the tibialis anterior was observed following the application of capsaicin. The amplitude and persistence of the F-waves were not significantly different after either intervention. Furthermore, although there was a change in motor unit recruitment, force steadiness did not change during the torque recordings.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0040.000

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 head0.028
GPT teacher head0.339
Teacher spread0.311 · 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 designBench or experimental
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTSpace→Same topicPain Mechanisms and Treatments→French-language works237,207→