Examination of Non-Specific Low Back Pain, Pain Perceptions and Disability Between Brazilian Jiu Jitsu, Muay Thai and Boxing Athletes
Bibliographic record
Abstract
Background: Non-specific low back pain is the leading cause of years lived with disability worldwide. The present study investigates non-specific low back pain, pain perceptions and disability due to pain among Brazilian Jiu Jitsu, Muay Thai and Boxing athletes. Methods: The study included 90 amateur athletes (aged 18–45 years; M = 28.97, SD = 5.88). The athletes completed the valid and reliable Pain Beliefs Perceptions Inventory (PBPI), the Quebec Pain Disability Scale (QPDS) and the Short-Form McGill Pain Questionnaire (SF-MPQ) which includes the Visual Analogue Scale (10 cm VAS 0–10 rating system) and the Present Pain Intensity index (PPI). Results: The results revealed that the majority of athletes rated their pain as low (SF-MPQ: M = 12.34, SD = 8.91; VAS: M = 1.65, SD = 1.82; PPI: M = 2.10, SD = 1.08) with low disability due to pain (QPDS: M = 18.98, SD = 22.71). Also, the majority of athletes disagreed that their pain was mysterious or persistent with high duration (PBPI: M = 1.43, SD = 2.23). Between the three martial arts, Brazilian Jiu Jitsu athletes showed statistically significantly (a) higher emotional and sensational pain intensity (x2(2) = 15.73; p < 0.001; x2(2) = 19.34; p < 0.001), (b) higher disability due to pain (x2(2)= 25.30; p < 0.001) and (c) more mysterious, more persistent pain with more duration (x2(2)= 9.32; p < 0.05) than Muay Thai and Boxing athletes. Also, a few correlations were found between age and pain perception only in Brazilian Jiu Jitsu and Boxing martial arts athletes. Conclusions: Further research is required to elucidate the biomechanical and psychological factors contributing to these differences between martial arts athletes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".