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Record W3007690189 · doi:10.1186/s12883-020-01644-0

An unusual presentation of neuropathic pain following cervical spinal cord injury: a case report

2020· article· en· W3007690189 on OpenAlexaff
Min Cheol Chang, Mathieu Boudier‐Revéret, Yoo Jin Choo, Ming‐Yen Hsiao

Bibliographic record

VenueBMC Neurology · 2020
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersNational Research Foundation of KoreaNational Taiwan University HospitalYeungnam UniversityNational Research FoundationHospital Research Foundation
KeywordsMedicineNeuropathic painNeurosurgeryPresentation (obstetrics)NeurologyNeurochemistrySpinal cord injurySpinal cordAnesthesiaPain medicineAnesthesiologySurgeryPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: We report a patient with unusual occipital neuropathic pain (at-level neuropathic pain) due to a small central cervical spinal cord injury (SCI). CASE PRESENTATION: A 50-year-old man presented with severe bilateral occipital pain after falling from a height of 2 m, 2 weeks ago. The degree of pain was evaluated to be 9 out of 10 using the numeric rating scale (NRS). The nature of the pain was tingling, burning, and piercing, and hyperalgesia was present over the bilateral posterior head regions. Greater occipital nerve block with bupivacaine and dexamethasone was not effective. On axial T2-cervical magnetic resonance imaging (MRI), a focal high signal change was observed in the central portion of the spinal cord at the C2 level. We deliberated that the patient's pain was due to the SCI observed on MRI, and after administration of oral medications, the NRS pain score reduced from 9 to 2. CONCLUSIONS: Neuropathic pain caused by SCI varies according to the location and degree of injury of the pain-related neural tracts; therefore, clinicians should closely observe the pain patterns and findings on imaging in patients with SCI to determine the cause of pain accurately.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.053
GPT teacher head0.356
Teacher spread0.304 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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