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Mechanisms of Behavioral Changes After Spinal Cord Injury

2019· reference-entry· en· W2914038977 on OpenAlexaff
Karim Fouad, Abel Torres‐Espín, Keith K. Fenrich

Bibliographic record

VenueOxford Research Encyclopedia of Neuroscience · 2019
Typereference-entry
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNeuroplasticitySpinal cord injuryNeuroscienceSpinal cordSpasticityAutonomic dysreflexiaMedicineBrainstemPsychologyPhysical medicine and rehabilitation

Abstract

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Abstract Spinal cord injury results in a wide range of behavioral changes including impaired motor and sensory function, autonomic dysfunction, spasticity, and depression. Currently, restoring lost motor function is the most actively studied and sought-after goal of spinal cord injury research. This research is rooted in the fact that although self-repair following spinal cord injury in adult mammals is very limited, there can be some recovery of motor function. This recovery is strongly dependent on the lesion size and location as well as on neural activity of denervated networks activated mainly through physical activity (i.e., rehabilitative training). Recovery of motor function is largely due to neuroplasticity, which includes adaptive changes in spared and injured neural circuitry. Neuroplasticity after spinal cord injury is extensive and includes mechanisms such as moderate axonal sprouting, the formation of new synaptic connections, network remapping, and changes to neuron cell properties. Neuroplasticity after spinal cord injury has been described at various physiological and anatomical levels of the central nervous system including the brain, brainstem, and spinal cord, both above and below injury sites. The growing number of mechanisms underlying postinjury plasticity indicate the vast complexity of injury-induced plasticity. This poses important opportunities to further enhance and harness plasticity in order to promote recovery. However, the diversity of neuroplasticity also creates challenges for research, which is frequently based on mechanistically driven approaches. The appreciation of the complexity of neuronal plasticity and the findings that recovery is based on a multitude and interlinked adaptations will be essential in developing meaningful new treatment avenues.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.135
GPT teacher head0.450
Teacher spread0.315 · 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.

Study designNot applicable
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

Citations6
Published2019
Admission routes1
Has abstractyes

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