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Record W2330399248 · doi:10.4103/2152-7806.137754

Resection of an oculomotor nerve cavernous angioma

2014· article· en· W2330399248 on OpenAlex
Sami Obaïd, Shu Li, Alexander G. Weil, Daniel J. Denis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSurgical Neurology International · 2014
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsUniversité de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsMedicineOculomotor nerveMagnetic resonance imagingSurgeryParalysisCranial nervesCranial nerve diseaseAngiomaOculomotor nerve palsyResectionRadiologyEye diseaseVascular diseasePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Cavernous angiomas (CAs) of cranial nerves are rare, and their occurrence on the third cranial nerve is particularly rare. Surgical management of such CAs involving the third nerve is controversial. We describe a case of a symptomatic CA of the oculomotor nerve and review the literature in order to ascertain the relevance of surgical intervention. CASE DESCRIPTION: A 71-year-old male patient presented with a 2-month history of progressive oculomotor nerve paralysis. CA of the oculomotor nerve was suspected on magnetic resonance imaging (MRI). The patient underwent complete resection of the CA through a subtemporal approach, preserving the integrity of the nerve. Histopathological analysis confirmed the diagnosis of CA. Despite optimal resection, the patient did not improve postoperatively. CONCLUSION: CAs of cranial nerves can cause rapid or progressive neurological deterioration. Whereas delayed treatment often leads to irreversible deficits, early nerve-sparing surgical excision of the CAs may potentially restore function.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.275
Teacher spread0.266 · 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