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Record W4282946005 · doi:10.1152/jn.00030.2022

The influence of experimental low back pain on neural networks involved in the control of lumbar erector spinae muscles

2022· article· en· W4282946005 on OpenAlexafffund
Antoine Rohel, Mikaël Desmons, Guillaume Léonard, Amélie Desgagnés, Rubens Alexandre da Silva, Martin Simoneau, Catherine Mercier, Hugo Massé‐Alarie

Bibliographic record

VenueJournal of Neurophysiology · 2022
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversité du Québec à ChicoutimiCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleCentres Intégré Universitaires de Santé et de Services SociauxCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité LavalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean
FundersFonds de Recherche du Québec - SantéFonds de recherche du QuébecRéseau Provincial de Recherche en Adaptation-RéadaptationNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsTranscranial magnetic stimulationLumbarMedicineLow back painMotor controlStimulationNeuromodulationNeuroscienceAnesthesiaPhysical medicine and rehabilitationPsychologyAnatomy

Abstract

fetched live from OpenAlex

In the presence of experimental low back pain, spinal, subcortical, and cortical motor networks involved in the control of back muscles were not modified. However, once the pain disappeared, a reduction in motoneuronal excitability was observed without change in corticospinal and vestibulospinal excitability, suggesting a reduction in descending drive. Experimental low back pain may elicit long-term plasticity even after pain extinction.

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.262
Teacher spread0.251 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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Same venueJournal of NeurophysiologySame topicMusculoskeletal pain and rehabilitationFrench-language works237,207