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Record W4256020552 · doi:10.14740/jmc3270

Clinical Features of Demyelinating Lesion in Medulla Oblongata and Cervical Cord Junction With the First Symptom of Neck Pain Misdiagnosed as Acute Stroke

2019· article· en· W4256020552 on OpenAlexvenueno aff
Yi Bao, Xingyue Li, Yajie Hu, Lixia Qin, Miao Zhang, Ying Wang, Guangjian Liu

Bibliographic record

VenueJournal of Medical Cases · 2019
Typearticle
Languageen
FieldMedicine
TopicSpinal Hematomas and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)LesionPhysical examinationNeck painNeurological examinationSpinal cordRadiologyMedulla oblongataMagnetic resonance imagingDifferential diagnosisSurgeryPathologyInternal medicineCentral nervous system

Abstract

fetched live from OpenAlex

This article aims to describe the clinical features of a demyelinating lesion in the medullary and cervical spinal cord junctions with a first symptom of neck pain misdiagnosed as acute stroke. The patient was initially misdiagnosed as ischemic stroke through stroke-like onset, clinical manifestations, physical examination, and brain computed tomography (CT); but the patient’s symptoms were not completely consistent with typical physical examination of stoke. Combined with brain magnetic resonance imaging (MRI) which showed demyelinating lesions and diagnostic treatment, the final diagnosis was the demyelinating lesion of the medulla oblongata and cervical cord junction. Acute hemiplegia in quiet state is not necessarily a stroke. Detailed physical examination and imaging data are helpful for differential diagnosis. Diagnostic treatment can help to confirm the diagnosis if necessary. Early use of methylprednisolone shock therapy combined with intravenous administration of gamma globulin for demyelinating disease can achieve good results, save limb function; and long-term medication is required to prevent rebound. J Med Cases. 2019;10(3):89-92 doi: https://doi.org/10.14740/jmc3270

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)0.002
metaresearch head score (Gemma)0.002
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 score0.111
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.357
Teacher spread0.330 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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