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Record W2889471742 · doi:10.3340/jkns.2017.0276

Review of the UBC Porcine Model of Traumatic Spinal Cord Injury

2018· article· en· W2889471742 on OpenAlexafffund
Kyoung‐Tae Kim, Femke Streijger, Neda Manouchehri, Kitty So, Katelyn Shortt, Elena B. Okon, Seth Tigchelaar, Peter A. Cripton, Brian K. Kwon

Bibliographic record

VenueJournal of Korean Neurosurgical Society · 2018
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesVancouver Spine Surgery InstituteUniversity of British Columbia
FundersNational Research Foundation of KoreaMinistry of Science, ICT and Future PlanningNational Research FoundationRick Hansen InstituteCraig H. Neilsen Foundation
KeywordsMedicineSpinal cord injuryTranslational researchAnimal modelRodent modelSpinal cordPreclinical testingPreclinical researchClinical trialBench to bedsideRat modelNeurosciencePathologyMedical physicsInternal medicineBiologyPsychiatry

Abstract

fetched live from OpenAlex

Traumatic spinal cord injury (SCI) research has recently focused on the use of rat and mouse models for in vivo SCI experiments. Such small rodent SCI models are invaluable for the field, and much has been discovered about the biologic and physiologic aspects of SCI from these models. It has been difficult, however, to reproduce the efficacy of treatments found to produce neurologic benefits in rodent SCI models when these treatments are tested in human clinical trials. A large animal model may have advantages for translational research where anatomical, physiological, or genetic similarities to humans may be more relevant for pre-clinically evaluating novel therapies. Here, we review the work carried out at the University of British Columbia (UBC) on a large animal model of SCI that utilizes Yucatan miniature pigs. The UBC porcine model of SCI may be a useful intermediary in the pre-clinical testing of novel pharmacological treatments, cell-based therapies, and the "bedside back to bench" translation of human clinical observations, which require preclinical testing in an applicable animal model.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.089
GPT teacher head0.401
Teacher spread0.312 · 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

Citations31
Published2018
Admission routes2
Has abstractyes

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