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Assessment of behavioural recovery following spinal cord injury in rats

2000· review· en· W2084088703 on OpenAlexaff
Gillian D. Muir, Aubrey A. Webb

Bibliographic record

VenueEuropean Journal of Neuroscience · 2000
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCategorizationSpinal cord injuryPsychologyPhysical medicine and rehabilitationNeurosciencePsychological interventionIntervention (counseling)Spinal cordMedicineComputer scienceArtificial intelligencePsychiatry

Abstract

fetched live from OpenAlex

Behavioural recovery is one of the primary goals of therapeutic intervention in animal models of disease. It is necessary, therefore, to have the means with which to quantify pertinent behavioural changes in experimental animals. Nevertheless, the number and diversity of behavioural measures which have been used to assess recovery after experimental interventions often makes it difficult to compare results between studies. The present review attempts to integrate and categorize the wide variety of behavioural assessments used to measure recovery in spinal-injured rats. These categories include endpoint measures, kinematic measures, kinetic measurements, and electrophysiological measurements. Within this categorization, we discuss the advantages and disadvantages of each type of measurement. Finally, we make some recommendations regarding the principles for a comprehensive behavioural analysis after experimental spinal cord injury in rats.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.227
GPT teacher head0.476
Teacher spread0.250 · 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 designSystematic review
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

Citations111
Published2000
Admission routes1
Has abstractyes

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