Cervical Electromyogram Profile Differences Between Patients of Neck Pain and Control
Bibliographic record
Abstract
STUDY DESIGN: A comparative analysis of electromyogram (EMG) signals of patients of cervical pain and normal controls. OBJECTIVES: To determine the differences between frequency and time domain parameters of EMG signals of patients of cervical pain and normal controls. SUMMARY OF BACKGROUND DATA: No diagnostic technique has emerged as a satisfactory tool for identification of spinal pain. METHOD: Seventeen male and 17 female chronic neck pain patients without cervical radiculopathy were recruited through neurology EMG clinic. The controls consisted of 30 male and 33 female subjects with no history of neck pain in the past 12 months. All subjects performed flexion, left anterolateral flexion, left lateral flexion, left posterolateral extension, and extension to pain threshold/20% maximum voluntary contraction and pain tolerance/maximum voluntary contraction in random order. The descriptive statistics for body weight normalized strength, normalized peak EMG, time to onset, time to peak, median frequency, mean power frequency, and frequency bands were calculated. These variables were subjected to analysis of variance and logistic regression to distinguish between patients and controls. RESULTS: The normalized peak EMG of patients was significantly greater than those of controls in both maximal and submaximal exertions (P < 0.01). Whereas there was no consistent pattern in time to peak EMG, the time to onset of EMG revealed that the left sternocleidomastoid was always recruited before the onset of torque. A lack of significant difference in the median frequency of the 2 samples indicates that the pain did not disturb the muscle conduction velocity. Using discriminant logistic regression on frequency domain and time domain parameters, up to 97% of patients and controls were correctly classified with the resubstitution method. CONCLUSION: Surface EMG can be used successfully in distinguishing chronic pain patients and controls, and efficacy of treatment regimes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".