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Record W29064166 · doi:10.1002/ppul.23784

Regenerating Nerve Fiber Innervation of Extraocular Muscles and Motor Functional Changes Following Oculomotor Nerve Injuries at Different Sites

2011· article· en· W29064166 on OpenAlexfundno aff
Wenchuan Zhang, Massimiliano Visocchi, Eduardo Marcos Fernandez Marquez

Bibliographic record

VenueNeural Regeneration Research · 2011
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsOculomotor nerveExtraocular musclesAnatomyMedicineSuperior orbital fissureOculomotor nucleusRetrograde tracingSuperior rectus muscleEye movementNerve injuryOptic nerveSurgeryCentral nervous systemOphthalmologyMidbrainCavernous sinus

Abstract

fetched live from OpenAlex

In the present study, the oculomotor nerves were sectioned at the proximal (subtentorial) and distal (superior orbital fissure) ends and repaired. After 24 weeks, vestibulo-ocular reflex evaluation confirmed that the regenerating nerve fibers following oculomotor nerve injury in the superior orbital fissure had a high level of specificity for innervating extraocular muscles. The level of functional recovery of extraocular muscles in rats in the superior orbital fissure injury group was remarkably superior over that in rats undergoing oculomotor nerve injuries at the proximal end (subtentorium). Horseradish peroxidase retrograde tracing through the right superior rectus muscle showed that the distribution of neurons in the nucleus of the oculomotor nerve was directly associated with the injury site, and that crude fibers were badly damaged. The closer the site of injury of the oculomotor nerve was to the extraocular muscle, the better the recovery of neurological function was. The mechanism may be associated with the aberrant number of regenerated nerve fibers passing through the injury site.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.158
GPT teacher head0.353
Teacher spread0.196 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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