[Prevalence and associated factors with pain in professional dancers].
Bibliographic record
Abstract
PURPOSE: To investigate factors associated to pain in ballet dancers as well its prevalence. METHODS: We conducted an analytical cross-sectional study among 141 professional ballet dancers from the main capitals of Northeastern Brazil. In order to evaluate the symptoms of pain we used Portuguese official versions of the McGill Protocol and the Pain Inventory of Wisconsin. For statistical analysis of the results we performed a descriptive assessment, followed by T-Student and Pearson's Correlation tests (taking a value of p < 0.05). RESULTS: We observed high levels of pain tolerance in 70.2% of the subjects, in which the intensity varied from moderate to severe. The lumbar region was the most affected (85.8%). We observed positive correlations concerning the intensity degree of pain with activities such as dancing (60.3%), sleeping (28.4%), marching (20.6%), general activities (32.6%), mood (27.7%), and personal relations (16.3%). CONCLUSION: We observed a high prevalence of pain in professional ballet dancers from the main Northeastern capitals, attacking mostly the lumbar region, followed by knees, neck, hip and feet, with substantial interference of pain symptoms in several activities of the personal and professional lives of these people.
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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 | 0.001 |
| 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".