Association Between Changes in Electromyographic Signal Amplitude and Abdominal Muscle Thickness in Individuals With and Without Lumbopelvic Pain
Bibliographic record
Abstract
STUDY DESIGN: Validation study. OBJECTIVES: To investigate the association between changes in electromyographic (EMG) signal amplitude and sonographic measures of muscle thickness of 4 abdominal muscles, during 2 clinical tests, in adults with and without lumbopelvic pain. BACKGROUND: There is a trend in rehabilitation to use ultrasound imaging (USI) to determine the extent of abdominal muscle contraction. However, the literature investigating the relationship between abdominal muscle thickness change and level of activation is inconclusive and has not included clinically relevant tasks. METHODS: Simultaneous recording from fine-wire EMG and USI was performed for 4 abdominal muscles, in 7 adults with lumbopelvic pain (mean ± SD age, 29.7 ± 12.0 years) and 7 adults without lumbopelvic pain (32.0 ± 10.6 years), during an active straight leg raise (ASLR) test and an abdominal drawing-in maneuver (ADIM). Cross-correlation functions and linear regression analyses were used to describe the relationship between the 2 measures. Analyses of variance were used to compare individuals with and without lumbopelvic pain, with an alpha set at .05. RESULTS: Across all muscles, peak cross-correlation values were low (ASLR, r = 0.28 ± 0.09; ADIM, r = 0.35 ± 0.11), and there was large variability in associated time lags (ASLR, τ = 0.69 ± 2.56 seconds; ADIM, τ = 0.53 ± 3.75 seconds). Regression analyses did not detect a systematic pattern of association between EMG signal amplitude and USI measurements, and analyses of variance revealed no differences between cohorts. CONCLUSION: These results suggest a weak relationship between EMG amplitude and abdominal muscle thickness change measured with USI during the ADIM and ASLR, and raise questions about thickness change derived from USI as a measure of muscular activity for the abdominal musculature.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".