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Record W2980150836 · doi:10.1136/bcr-2019-232291

Nasopharyngeal carcinoma presenting as a sixth nerve palsy and Horner’s syndrome

2019· article· en· W2980150836 on OpenAlexaff
Hatim Batawi, Jonathan A. Micieli

Bibliographic record

VenueBMJ Case Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDiplopiaCavernous sinusHorner syndromePalsyCranial nervesSixth nerve palsyNasopharyngeal carcinomaAbducens nerveSurgerySkullSinus (botany)RadiologyAnatomyRadiation therapyPathology

Abstract

fetched live from OpenAlex

The combination of a sixth nerve palsy and ipsilateral Horner's syndrome localises the disease process to the posterior cavernous sinus and can be a result of various pathologies in this region. A 74-year-old Chinese woman presented with a 9-month history of binocular horizontal diplopia worse when looking left. She was found to have a left sixth nerve palsy and Horner's syndrome and MRI revealed an enhancing soft tissue mass in the nasopharynx with involvement of the bones of the skull base and invasion of the left cavernous sinus. Endoscopic biopsy of the mass confirmed the diagnosis of non-keratinising squamous cell carcinoma, which was Epstein-Barr virus positive. She was treated with radiation therapy. Patients with a sixth nerve palsy and ipsilateral Horner's syndrome should have urgent neuroimaging with careful attention to the cavernous sinus since sympathetic fibres join the sixth nerve for a short distance in this location.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.293
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations6
Published2019
Admission routes1
Has abstractyes

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