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Chronic Electrical Nerve Stimulation as a Therapeutic Intervention for Peripheral Nerve Repair

2015· article· en· W2897049090 on OpenAlexfundno aff
Miyuki Sakuma, Ivan R. Minev, Sandra Gribi, Bhagat Singh, Clifford J. Woolf, Stéphanie P. Lacour

Bibliographic record

VenueBioelectronic Medicine · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsReinnervationNerve repairRegeneration (biology)StimulationMedicineNeurosciencePeripheral nerve injuryPeripheral nerveNerve injuryAnesthesiaSurgeryPsychologyBiologyAnatomy

Abstract

fetched live from OpenAlex

When a peripheral nerve is injured after either trauma or a neurodegenerative disease, motor function and sensory perception are impaired. Repair strategies aim both at reconstructing the damaged nerve and in promoting regeneration to enhance target reinnervation and functional recovery. Advanced surgical procedures can enable efficient repair, but restoration of function remains challenging. Among various factors influencing nerve regeneration, electrical stimulation is often cited as a potential therapeutic approach to nerve repair, engaging regenerative transcriptional programs. In this report, we review both reported effects on axonal growth and functional outcomes of electrical stimulation on peripheral nerve repair and the techniques for chronic nerve stimulation, highlighting the challenges and opportunities of such repair strategies.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.347
Teacher spread0.288 · 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
Published2015
Admission routes1
Has abstractyes

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