Reliability of electromyographic and force measures during prone isometric back extension in subjects with and without low back pain
Bibliographic record
Abstract
Maximal voluntary isometric activations (MVIA) are frequently used as inputs for models attempting to predict muscle force and as normalization values in studies assessing muscle function. However, pain may adversely affect maximal muscle activation. The purpose of this study was to assess reliability of MVIA force and electromyographic (EMG) activity during prone isometric back extension in subjects with and without low back pain (LBP). A novel sub-maximal method using the percentages of the estimated mass of the head-arms-trunk (HAT) segment was also investigated. Repeated measures on 20 male volunteers divided into an LBP (n=10) and a control group (n=10) were made on 4 occasions. Force and EMG activity were recorded bilaterally from upper lumbar erector spinae (ULES), lower lumbar erector spinae (LLES), and biceps femoris (BF). Subjects exerted a maximal extension effort against a harness assembly that was attached to a force transducer. Submaximal exertions were also performed with an additional resistance of 100%, 110%, 120%, 130%, 140%, 150%, 160%, and 170% of HAT. Mean MVIA forces were significantly (p 0.90), but were significantly less in LBP (R=0.36-0.80). EMG of BF demonstrated excellent reliability across both groups (R>0.90). The resistance at 100% HAT demonstrated the highest reliability for LBP patients, whereas higher percentages of HAT showed either similar or higher reliability for controls. Force output and back EMG activity are less reliable with LBP individuals and should be taken into consideration when testing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".