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Record W1964723804 · doi:10.1109/tnsre.2014.2307756

Controlled Nerve Ablation With Direct Current: Parameters and Mechanisms

2014· article· en· W1964723804 on OpenAlexafffund
Einat Ravid, A. Procházka

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2014
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineAblationSpasticSpinal cord injuryAnesthesiaPhysical medicine and rehabilitationSpinal cordCardiologyCerebral palsy

Abstract

fetched live from OpenAlex

Spastic hypertonus (muscle over-activity) often develops after spinal cord injury or stroke. Chemodenervating agents such as Botulinum toxin A (BtA) and phenol are often used to treat this condition. We have previously shown that the use of direct current (DC) to create controlled lesions of peripheral nerves may provide a means of reducing spastic hypertonus. Here, we explored a range of stimulation parameters that could be used clinically. Nerves were lesioned with DC in chronically implanted animals and the outcome was tracked over many months. In addition, we used DC to ablate nerves in animals with decerebrate rigidity (an animal model of spastic hypertonus) and we explored the possible mechanisms of DC nerve ablation. We found that nerve ablation with DC was effective in reducing hypertonus. Some stimulation paradigms were more likely to be clinically acceptable than others. Furthermore we showed that nerve regeneration occurs in the months following DC nerve ablation and we demonstrated that the ablation procedure is repeatable, much like BtA treatment. Regarding mechanism, our results did not support the hypothesis that DC caused nerve damage by overactivating sodium channels. Rather, the mechanism of damage seems to be related to changes in pH.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.399

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 head0.007
GPT teacher head0.210
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations9
Published2014
Admission routes2
Has abstractyes

Explore more

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