Effect of pelvic stabilization and hip position on trunk extensor activity during back extension exercises on a roman chair
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of pelvic stabilization and hip position on the electromyographic activity of trunk extensors during Roman chair exercise. A secondary objective was to compare genders. DESIGN: Repeated measures. SUBJECTS: Eleven men and 11 women volunteers. METHODS: Five trunk flexion-extension cycles for 3 Roman chair conditions: (i) pelvis unrestrained; (ii) pelvis restrained; and (iii) hip at 40 degrees flexion. Electromyographic signals were recorded on the back muscles, as well as on the gluteus maximus and biceps femoris. The percentage of electromyographic amplitude relative to the maximal activity was used to assess the level of muscular activation of each muscle group across the exercises. RESULTS: For both genders, the Roman chair conditions did not influence the activity of the back and gluteus muscles. The hip-at-40 degrees-flexion condition significantly reduced the activity of the biceps femoris (average of 4-18%) relative to the other 2 conditions. Gender differences were observed on the activity of the biceps femoris in all Roman chair conditions. CONCLUSION: The hip-at-40 degrees-flexion condition would allow the Roman chair exercise to train the targeted back muscles more specifically by overloading them over a longer duration in order to induce physiological changes.
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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) | 0.000 | 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 | 0.000 | 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".