Association of Back Pain with Spinal Hypermobility and Static Trunk Endurance in Ballroom Dancers
Bibliographic record
Abstract
Background. Low back pain is one of the most common problems among ballroom dancers. The aim. To determine the correlations between the hypermobility of the lumbar spine, lower back pain and static endurance of the trunk muscles in sports dancers. Methods. 36 ballroom dancers (50% males and 50% females) between the ages of 16 and 22 participated in the study. The subjects filled out a questionnaire, inclinometry was performed to determine the amplitudes of spinal movements, the static endurance of the trunk muscles was assessed according to the McGill methodology, and the intensity of the felt pain was assessed on the analog pain scale. Results. 72.2% of dancers have experienced lower back pain at least once in their life. The average back pain intensity for males was 2.17 ± 2.92 p., and for females – 3.56 ± 2.66 (p< 0.05). The amplitude of back extension was 28.1° for dancers with back pain, and 22.2° for those without. The average endurance of the back muscles of the subjects who felt pain was 101s, and 117s for those who did not. The average endurance of the left side of the trunk muscles was 70s in subjects with low back pain and 83s in subjects without. A statistically significant difference was found between pain sensation and extension amplitude (p<0.05) and pain sensation and static endurance of trunk muscles (p<0.05). Conclusion. The more intense the pain, the greater the amplitude of the spine. With lower back pain the static endurance of the muscles of the back and left side of the trunk was shorter. Keywords: hypermobility of the spine, back pain, static endurance of the trunk muscles, sports dancing.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".