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MR properties of rat sciatic nerve following trauma

2001· article· en· W2073169643 on OpenAlexaff
Greg J. Stanisz, Rajiv Midha, Catherine A. Munro, R. Mark Henkelman

Bibliographic record

VenueMagnetic Resonance in Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsSciatic nerveFractional anisotropyMagnetization transferMedicineHistopathologyDegeneration (medical)AnatomyRegeneration (biology)T2 relaxationNerve injuryDiffusion MRIAxonal degenerationPathologyMagnetic resonance imagingAnesthesiaRadiologyBiology

Abstract

fetched live from OpenAlex

T(1) and T(2) relaxation times, magnetization transfer (MT), and diffusion anisotropy of rat sciatic nerve were measured at different time intervals following trauma. The nerve injury was induced by either cutting (irreversible nerve degeneration) or crushing (degeneration followed by regeneration). The MR properties were measured for proximal and distal portions of the injured nerve. The portions of the nerve proximal to the induced injury exhibited MR characteristics similar to those of normal nerves, whereas the distal portions showed significant differences in all MR parameters. These differences diminished in the regenerating nerves within approximately 4 weeks post injury. In the case of irreversible nerve damage, the differences in the distal nerves were slightly larger and did not resolve even 6 weeks after induced trauma. The MR measurements were correlated with histopathology exams. Observed changes in tissue microstructure, such as demyelination, inflammation, and axonal loss, can result in a significant increase in the average T(1) and T(2) relaxation times, reduction in the MT effect, and decrease in diffusion anisotropy. MR parameters, therefore, are very good indicators of nerve damage and may be useful in monitoring therapies that assist nerve regeneration. Magn Reson Med 45:415-420, 2001.

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)0.000
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 score0.499
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.081
GPT teacher head0.342
Teacher spread0.261 · 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

Citations98
Published2001
Admission routes1
Has abstractyes

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