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Record W4315753541 · doi:10.1177/10738584221145570

Mapping the Iceberg of Autonomic Recovery: Mechanistic Underpinnings of Neuromodulation following Spinal Cord Injury

2023· review· en· W4315753541 on OpenAlexafffund
Soshi Samejima, Claire Shackleton, Tiev Miller, Chet T. Moritz, Thomas M. Kessler, Klaus Krogh, Rahul Sachdeva, Andrei V. Krassioukov

Bibliographic record

VenueThe Neuroscientist · 2023
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsGF Strong Rehabilitation CentreVancouver Coastal HealthInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersSteno Diabetes Center AarhusCanadian Institutes of Health ResearchInternational Spinal Research TrustCanada Foundation for InnovationRick Hansen FoundationWings for LifeMichael Smith Health Research BCParalyzed Veterans of AmericaRick Hansen InstituteParalyzed Veterans of America Research FoundationU.S. Department of Defense
KeywordsSpinal cord injuryNeuromodulationMedicineSpinal cordNeuroscienceAutonomic nervous systemNeuroplasticityStimulationPsychologyBlood pressureHeart rateInternal medicine

Abstract

fetched live from OpenAlex

Spinal cord injury leads to disruption in autonomic control resulting in cardiovascular, bowel, and lower urinary tract dysfunctions, all of which significantly reduce health-related quality of life. Although spinal cord stimulation shows promise for promoting autonomic recovery, the underlying mechanisms are unclear. Based on current preclinical and clinical evidence, this narrative review provides the most plausible mechanisms underlying the effects of spinal cord stimulation for autonomic recovery, including activation of the somatoautonomic reflex and induction of neuroplastic changes in the spinal cord. Areas where evidence is limited are highlighted in an effort to guide the scientific community to further explore these mechanisms and advance the clinical translation of spinal cord stimulation for autonomic recovery.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.951
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.219
GPT teacher head0.437
Teacher spread0.218 · 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 designOther design
Domainnot available
GenreReview

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

Citations26
Published2023
Admission routes2
Has abstractyes

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