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Record W4416807024 · doi:10.1055/s-0045-1812893

Cervical radiculopathy for neurologists: the role of electrodiagnosis

2025· article· en· W4416807024 on OpenAlexaff
Lucas Immich Gonçalves, Pedro Helder de Oliveira, José Pedro Soares Baima

Bibliographic record

VenueArquivos de Neuro-Psiquiatria · 2025
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsElectromyoneurographyElectromyographyElectrodiagnosisCervical radiculopathyDenervationNeurologyNerve conductionCarpal tunnel syndrome

Abstract

fetched live from OpenAlex

Cervical radiculopathy (CR) is a common condition encountered in the general population, usually related to a musculoskeletal degenerative condition. Conventional electroneuromyography (ENMG) consists of nerve conduction studies (NCS) and needle electromyography (EMG), and it is regarded as the most specific diagnostic evaluation in this scenario. Although CR is commonly encountered in clinical practice, ENMG as a diagnostic tool is not often discussed in neurology residency programs. Electromyography has demonstrated modest sensitivity (50-71%) but excellent specificity (approaching 100%) for the diagnosis of CR. It can also provide valuable information about lesion chronicity. In EMG, acute lesions typically present with denervation potentials and reduced recruitment, but with preserved motor unit action potential (MUAP) morphology. In contrast, chronic lesions are characterized by remodeling, with MUAPs showing increased duration, amplitude, and number of phases, in addition to reduced recruitment. The present review aims to provide an overview of the roles of NCS and EMG, while also introducing key terminology commonly encountered in the interpretation of these diagnostic modalities.

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)0.000
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 score0.394
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.270
Teacher spread0.264 · 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
Published2025
Admission routes1
Has abstractyes

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