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Record W2102514815 · doi:10.4021/jnr.v3i5.233

MRI Findings of Oculomotor Nerve Palsy in Mild Traumatic Brain Injury: Case Report and Review of the Literature

2013· article· en· W2102514815 on OpenAlexvenueno aff
Aldo Berti, Pedro M. Ramirez, Hoan Tran

Bibliographic record

VenueJournal of Neurology Research · 2013
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsOculomotor nerve palsyMedicineOculomotor nervePalsyPtosisTraumatic brain injuryMagnetic resonance imagingBrainstemPupilSurgeryRadiologyPathologyPsychologyNeuroscienceInternal medicine

Abstract

fetched live from OpenAlex

Isolated traumatic oculomotor nerve palsy associated with mild traumatic brain injury (TBI) is rarely reported in literature and has not been associated with magnetic resonance imaging (MRI) findings until this case report. We report the case of a 55-year-old man with complete left side traumatic oculomotor nerve palsy, 7 mm non-reactive dilated pupil, and ptosis after a helmeted bicycle accident. Mild sulcal subarachnoid hemorrhage was seen on computed tomography (CT) at presentation. MRI during hospitalization showed contrast enhancement of the cisternal segment of the left oculomotor nerve, near the rootlets as it exits the brainstem. He was followed for 18 months with near resolution of oculomotor nerve palsy, but persisting enhancing lesion on MRI at the third nerve outlet from the brainstem, suggestive of partial avulsion. Isolated oculomotor nerve palsy after mild TBI is extremely rare. After review of the literature, we are the first to show radiographic evidence of injury to the oculomotor nerve in a patient with mild TBI . MRI studies may be helpful in a patient with isolated oculomotor nerve palsy to identify the type of injury to the nerve, to rule out underlying pathology concurrent with the TBI , and may also indicate prognosis for recovery. doi: http://dx.doi.org/10.4021/jnr233w

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.073
GPT teacher head0.423
Teacher spread0.349 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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