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Animal Models for Studying Potential Training Strategies in Persons with Spinal Cord Injury

2005· article· en· W1970665107 on OpenAlexaff
Laurent J. Bouyer

Bibliographic record

VenueJournal of Neurologic Physical Therapy · 2005
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsSpinal cordSpinal cord injuryMedicinePhysical medicine and rehabilitationRehabilitationAnimal modelLesionTreadmillNeurosciencePsychologyPhysical therapySurgery

Abstract

fetched live from OpenAlex

In the late 1980s, it was clearly demonstrated that adult spinal cats can be re-trained to walk after a complete spinal cord transection, using treadmill training. This has led to profound changes in the rehabilitation of persons with spinal cord injury. The use of animal models to study training-induced locomotor plasticity after spinal cord injury has expanded since this original demonstration. The goal of the present review is to summarize findings obtained with these animal models that may be of relevance to the re-training of humans with spinal cord injury. From the complete spinal cord transection models, adaptive capacity, retention of training, task-specificity, role of cutaneous inputs, effect of training with robotic devices, and spinal cord stimulation will be discussed. From the partial spinal lesion models, the effect of ventral or dorsal lesions of the cord will be presented. Finally, the effects of drugs on training will be compared between the complete and partial spinal lesions models.

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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.177
GPT teacher head0.429
Teacher spread0.252 · 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 designNon-randomized trial
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

Citations25
Published2005
Admission routes1
Has abstractyes

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