MétaCan
Menu
Back to cohort

Cervical Electromyogram Profile Differences Between Patients of Neck Pain and Control

2007· article· en· W2074114197 on OpenAlexaff
Shrawan Kumar, Yogesh Narayan, Narasimha Prasad, Ashfaq Shuaib, Zaeem A. Siddiqi

Bibliographic record

VenueSpine · 2007
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineNeck painElectromyographyLogistic regressionPhysical therapyAnalysis of varianceNeurologyPhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.259
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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

Citations37
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueSpineSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207