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Record W1599320441 · doi:10.4236/ojvm.2015.57022

Humane Non-Human Primate Model of Traumatic Spinal Cord Injury: Quantitative Analysis of Electromyographic Data

2015· article· en· W1599320441 on OpenAlexaff
Nitin Seth, Farah Masood, John B. Sledge, William A. Graham, Douglas L. Rosene, Susan V. Westmoreland, Shiela Macri, Ervin Sejdić, Amber Hoggatt, Heather A. Simmons, Hussein A. Abdullah, Shanker Nesathurai

Bibliographic record

VenueOpen Journal of Veterinary Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsMcMaster UniversityHamilton Health SciencesUniversity of Guelph
Fundersnot available
KeywordsNon human primateMacaqueSpinal cord injurySpinal cordMetric (unit)PrimatePhysical medicine and rehabilitationRhesus macaqueMedicineAnimal modelWeaknessHindlimbAnatomyNeurosciencePsychologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

A valid non human primate model of traumatic spinal cord injury (TSCI) is essential to evaluate and develop new treatments. In previous experiments, it has been demonstrated that a transmitter can be implanted in the macaque fasicularis monkey that measures electromyographic data from the musculature of the tail. As well, previous experiments have demonstrated that selective lesions can be created in the lower thoracic spinal cord that does not cause limb weakness and/or bowel dysfunction. The histopathological features of these lesions appear similar to human TSCI. This paper describes a method by which the EMG data can be transformed into a quantitative metric of volitional limb movement (“Q”). This metric permits an objective assessment of injury, natural recovery as well as potential efficacy of candidate treatments.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.462
GPT teacher head0.572
Teacher spread0.110 · 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 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
Published2015
Admission routes1
Has abstractyes

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