Cervical radiculopathy for neurologists: the role of electrodiagnosis
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".