Clinical Predictors of EMG-confirmed Cervical and Lumbosacral Radiculopathy
Bibliographic record
Abstract
BACKGROUND: Electromyography (EMG) for suspected cervical or lumbosacral root compression is often negative, producing expense and physical discomfort that could have been avoided. To improve patient selection for testing, we sought to identify clinical features that would accurately predict presence of radiculopathy on EMG. METHODS: Adult patients consecutively evaluated for suspected cervical or lumbosacral root compression at an academic clinical neurophysiology laboratory were prospectively enrolled. Presence of clinical features suggesting root disease (neck or back pain, dermatomal pain or numbness, myotomal weakness, segmental reflex loss, and straight leg-raising) was recorded prior to testing. EMG examination to confirm root compression was conducted per standard protocols. Analysis was based on computation of sensitivity, specificity, predictive values, and accuracy. RESULTS: A total of 200 patients (55% male; mean age 46.4 years; 38% suspected of cervical and 62% of lumbosacral disease) were included. EMG evidence of root disease was detected in 31% of cervical and 62% of lumbosacral referrals. Dermatomal pain was the most sensitive, and segmental reflex loss and myotomal weakness the most specific individual predictors of root disease. Combined presence of dermatomal pain or numbness with segmental reflex loss and myotomal weakness approached specificities of 78% (lumbosacral disease) and 99% (cervical disease). In all cases, myotomal weakness was the most accurate predictor of root disease. CONCLUSION: The diverse symptoms and signs of cervical and lumbosacral root compression predict a positive electrodiagnosis of radiculopathy with varying degrees of accuracy, and may be used to guide patient selection for EMG testing.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".