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Record W4415103994 · doi:10.1097/wco.0000000000001433

Overlapping mechanisms of epidural spinal cord stimulation for pain control and movement recovery

2025· article· en· W4415103994 on OpenAlexaff
Evan F. Joiner, Marom Bikson, Jason B. Carmel

Bibliographic record

VenueCurrent Opinion in Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsColumbia College
FundersCongressionally Directed Medical Research ProgramsNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Institute on Drug AbuseNational Institutes of Health
KeywordsMovement (music)Movement controlPain controlSpinal cordSpinal cord stimulationElectromyography

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Spinal cord stimulation (SCS) for pain control and movement recovery have developed under parallel conceptual frameworks. SCS for pain has traditionally targeted the dorsal columns, while SCS for movement recovery has targeted the large-diameter afferent fibers near the dorsal root entry zone. We review the evidence to support these parallel mechanistic frameworks and explore potential mechanistic overlap between the two fields. RECENT FINDINGS: Recent advances in closed-loop stimulation for pain and dorsal root (DR) stimulation for movement recovery speak to the value of these parallel mechanistic models in each field. However, review of the devices, electrode placement, and stimulation parameters used in both fields reveals overlap in the doses of SCS considered effective in each. Furthermore, evidence from finite element modeling suggests overlapping recruitment of dorsal column and dorsal root fibers from both midline and lateral stimulation. SUMMARY: There is evidence to support overlapping mechanisms of SCS for pain and movement recovery. The implications of potential mechanistic overlap warrant further investigation.

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: none
Teacher disagreement score0.627
Threshold uncertainty score0.393

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 head0.042
GPT teacher head0.350
Teacher spread0.308 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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