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Record W4387884116 · doi:10.4236/ojvm.2023.1310016

Correlation of Pathological Findings with MRI Imaging in Traumatic Spinal Cord Injury in Hyperacute Time Period in Non Human Primate Model

2023· article· en· W4387884116 on OpenAlexaff
Heather A. Simmons, Kevin M. Johnson, Dane Schalk, Kevin Brunner, Puja Basu, Casey Fitz, Omar Fayez, Saverio Capuano, Shanker Nesathurai

Bibliographic record

VenueOpen Journal of Veterinary Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsMagnetic resonance imagingMedicineRadiological weaponPrimatePathologicalRadiologySpinal cordPathologyTraumatic brain injurySpinal cord injuryNeurosciencePsychology

Abstract

fetched live from OpenAlex

Traumatic Spinal Cord Injury (TSCI) remains a significant cause of human suffering. The World Health Organization estimates that there are between 250,000 to 500,000 new cases every year. Magnetic Resonance Imaging (MRI) has been an important advancement in the diagnosis and management of TSCI. Nevertheless, there is a lack of literature correlating the radiological abnormalities and histopathological findings in the first hour (i.e., hyperacute period) after injury. The aim of this preliminary study is to elucidate the relationship between the MRI abnormalities and histopathological abnormalities in the hyperacute time period. In this study, a non-human primate model (NHP) primate model is used to characterize the histopathological and radiological features. Specifically, an experimental TSCI is created with an epidural catheter. This is followed by MRI imaging. The subject is then humanely euthanized and a post-mortem examination is completed. These results suggest that the noted radiological abnormalities are consistent with a combination of hemorrhage, edema as well as eosinophilic cellular matter in the central canal.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.114
GPT teacher head0.468
Teacher spread0.353 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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