MétaCan
Menu
Back to cohort
Record W2885666522 · doi:10.1089/neu.2018.5906

Rehabilitative Training in Animal Models of Spinal Cord Injury

2018· review· en· W2885666522 on OpenAlexaff
Abel Torres‐Espín, Éric Beaudry, Keith K. Fenrich, Karim Fouad

Bibliographic record

VenueJournal of Neurotrauma · 2018
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsWomen and Children’s Health Research Institute
Fundersnot available
KeywordsSpinal cord injuryMedicineNeuroplasticityPhysical medicine and rehabilitationAnimal modelPreclinical researchNeuroscienceRehabilitationTraining (meteorology)Spinal cordPsychologyPhysical therapy

Abstract

fetched live from OpenAlex

Rehabilitative motor training is currently one of the most widely used approaches to promote moderate recovery following injuries of the central nervous system. Such training is generally applied in the clinical setting, whereas it is not standard in preclinical research. This is a concern as it is becoming increasingly apparent that neuroplasticity enhancing treatments require training or some form of activity as a co-therapy to promote functional recovery. Despite the importance of training and the many open questions regarding its mechanistic consequences, its use in preclinical animal models is rather limited. Here we review approaches, findings and challenges when training is applied in animal models of spinal cord injury, and we suggest recommendations to facilitate the integration of training using an appropriate study design, into pre-clinical studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.433
GPT teacher head0.533
Teacher spread0.100 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations47
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of NeurotraumaSame topicSpinal Cord Injury ResearchFrench-language works237,207