Associations between the severity of obstructive lower urinary tract symptoms and care-seeking behavior in rural Africa: A cross-sectional survey from Uganda
Bibliographic record
Abstract
STUDY TYPE: A cross sectional survey. BACKGROUND: Global estimates indicate that by 2018 2.3 billion individuals worldwide will suffer from lower urinary tract symptoms (LUTS), with 1.1 billion having LUTS related to bladder outlet obstruction (BOO). Left untreated BOO in men causes irreversible changes to the urinary tract leading to urinary retention, the need for catheterization, renal failure and even death. Estimates suggest that Africa will be one of the continents with the greatest increase in (LUTS) by 2018 however direct measures in Africa are lacking. The objectives were to: (1) measure of prevalence of LUTS/BOO in a community-based sample of men in Africa, (2) compare community-based LUTS/BOO frequency to those seeking care for LUTS in a local clinic (3) quantify bother, interference with daily living, worry and quality of life related to LUTS/BOO between community and clinic settings and (4) examine relationships between socioeconomic and demographics related to LUTS/BOO. METHODS AND FINDINGS: 473 men from a rural Ugandan community (238 residents living with their symptoms and 177 presenting at a clinic for care) completed the International Prostate Symptom Scale (IPSS) and a 53-item validated LUTS symptom, bother and quality of life index. Severity of symptoms was categorized based on reference ranges for mild, moderate and severe levels of the IPSS, comparing those in the community versus those seeking care for symptoms. IPSS indicated that 55.9% of men in the community versus 17.5% of those at the clinic had mild symptoms, 31.5% in the community versus 52.5% of those at the clinic had moderate symptoms and 12.6% of those in the community versus 29.9% of those at the clinic had severe symptoms (p<0.001). Men seeking care for LUTS/BOO had a lower quality of life (p<0.05), were more bothered by their urinary symptoms (p<0.05), had more interference with daily activity and worry (p<0.05) but this did not have an impact on their general sense of wellbeing. CONCLUSIONS: The burden of disease of LUTS/BOO in this rural African cohort is high and significantly higher among those seeking care due to the bother of their symptoms. One in 4 men will spend money for transport to clinic due to LUTS/BOO despite low economic resources. Educational tools for patients structured to the level of literacy are justified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".