Prevalence of urinary incontinence in Brazilian para athletes
Bibliographic record
Abstract
OBJECTIVE: To describe the prevalence of urinary incontinence (UI) in para athletes in Brazil. METHODS: This is a cross-sectional study with Brazilian para athletes with physical impairments from all para sports. The data from 86 participants of both sexes (60 males and 26 females) were collected through an online survey that gathered sociodemographic data and the International Consultation on Incontinence Questionnaire-Urinary Incontinence Short Form, from March to July 2023. RESULTS: The prevalence of UI was 45.3% (n=39), with the average impact on quality of life scored at 6.1±3.5 on a scale of 0-10. Most para athletes reported moderate (43.5%) or severe (38.4%) symptoms. The most common type was mixed UI (46.1%), with an average of 3±1.9 episodes of urinary loss per athlete in the last 4 weeks. Adjusted Poisson regression (controlling for sex, age and level of competition) revealed that para athletes with orthopaedic impairments had a 58% lower prevalence of UI (prevalence ratio=0.42; 95% CI 0.24, 0.83) compared with those with neurological impairments. Furthermore, ordinal regression indicated that para athletes with neurological impairments were 147% more likely to experience a progression from 'severe' to 'very severe' UI (OR=2.47; 95% CI 1.59, 3.93). CONCLUSIONS: UI is highly prevalent among para athletes, particularly those with neurological impairments, underscoring the need for specialised genitourinary healthcare and the need for further treatment and monitoring of the condition. There is a critical need to raise awareness among coaches, healthcare providers and the athletes themselves about UI and its impact to foster the comprehensive well-being of these 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 | 0.000 | 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".