Assessment of neuromuscular and haemodynamic activity in individuals with and without chronic low back pain
Bibliographic record
Abstract
BACKGROUND: Biering-Sørenson (1984) found that individuals with less lumbar extensor muscle endurance had an increased occurrence of first episode low back pain. As a result, back endurance tests have been recommended for inclusion in health assessment protocols. However, different studies have reported markedly different values for endurance times, leading some researchers to believe that the back is receiving support from the biceps femoris and gluteus maximus. Therefore, this study was designed to examine the haemodynamic and neuromuscular activity of the erector spinae, biceps femoris, and gluteus maximus musculature during the Biering-Sørenson Muscular Endurance Test (BSME). METHODS: Seventeen healthy individuals and 46 individuals with chronic low back pain performed the Biering-Sørenson Muscular Endurance Test while surface electromyography was used to quantify neuromuscular activity. Disposable silver-silver-chloride electrodes were placed in a bipolar arrangement over the right or left biceps femoris, gluteus maximus, and the lumbosacral paraspinal muscles at the level of L3. Near Infrared Spectroscopy was used simultaneously to measure tissue oxygenation and blood volume changes of the erector spinae and biceps femoris. RESULTS: The healthy group displayed a significantly longer time to fatigue (Healthy: 168.5s, LBP: 111.1s; p < or = 0.05). Significant differences were shown in the median frequency slope of the erector spinae between the two groups at 90-100% of the time to fatigue while no significant differences were noted in the haemodynamic data for the two groups. CONCLUSION: Although the BSME has been recognized as a test for back endurance, individuals with chronic LBP appear to incorporate a strategy that may help support the back musculature by utilizing the biceps femoris and gluteus maximus to a greater degree than their healthy counterparts.
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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 | 0.001 |
| 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".