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Record W4402406787 · doi:10.4103/nrr.nrr-d-24-00369

Stepping up after spinal cord injury: negotiating an obstacle during walking

2024· article· en· W4402406787 on OpenAlexaff
Alain Frigon, Charly G. Lecomte

Bibliographic record

VenueNeural Regeneration Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsSpinal cord injurySpinal cordObstacleNeurosciencePhysical medicine and rehabilitationPremotor cortexMotor cortexBalance (ability)Posterior parietal cortexSensory systemCortex (anatomy)GaitObstacle avoidancePsychologyMedicineComputer scienceAnatomyRobotArtificial intelligence

Abstract

fetched live from OpenAlex

Every day walking consists of frequent voluntary modifications in the gait pattern to negotiate obstacles. After spinal cord injury, stepping over an obstacle becomes challenging. Stepping over an obstacle requires sensorimotor transformations in several structures of the brain, including the parietal cortex, premotor cortex, and motor cortex. Sensory information and planning are transformed into motor commands, which are sent from the motor cortex to spinal neuronal circuits to alter limb trajectory, coordinate the limbs, and maintain balance. After spinal cord injury, bidirectional communication between the brain and spinal cord is disrupted and animals, including humans, fail to voluntarily modify limb trajectory to step over an obstacle. Therefore, in this review, we discuss the neuromechanical control of stepping over an obstacle, why it fails after spinal cord injury, and how it recovers to a certain extent.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.159
GPT teacher head0.491
Teacher spread0.332 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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