Examining lower urinary tract symptom constellations using cluster analysis
Bibliographic record
Abstract
OBJECTIVE: To gain a better understanding of how patients experience lower urinary tract symptoms (LUTS) and to determine whether particular symptoms cluster together, as LUTS seldom occur alone. SUBJECTS AND METHODS: A secondary analysis of a cross-sectional, population-based survey of adults in Sweden, Italy, Germany, UK and Canada was undertaken to examine the presence of LUTS groups. Of the 19,165 telephone surveys, 13,519 respondents reported at least one LUTS and were included in the analysis. All respondents were asked about the presence of 14 LUTS (International Prostate Symptom Score plus seven additional LUTS). K-means cluster analyses, a statistical method for sorting objects into groups so that similar objects are grouped together, was used to identify groups of people based on their symptoms. Men and women were analysed separately. A split-half random sample was selected from the dataset so that exploratory analyses could be conducted in one half and confirmed in the second. On model confirmation, the sample was analysed in its entirety. RESULTS: Included in this analysis were 5014 men (mean age 49.8 years; 95% white) and 8505 women (mean age 50.4 years; 96% white). Among both men and women, six distinct symptom cluster groups were identified and the symptom patterns of each cluster were examined. For both, the largest cluster consisted of respondents with minimal symptoms (i.e. reporting essentially one symptom), 56% of men and 57% of women. The remaining five clusters for men and women were labelled based on their predominant symptoms. For men, the clusters were nocturia of twice or more per night (12%); terminal dribble (11%); urgency (10%); multiple symptoms (9%); and postvoid incontinence (5%). For women, the clusters were nocturia of twice or more per night (12%); terminal dribble (10%); urgency (8%); stress incontinence (8%); and multiple symptoms (5%). The multiple-symptom groups had several and varied LUTS, were older, and had more comorbidities. Clusters of terminal dribble and male postvoid incontinence had a lower prevalence of all other LUTS, but were fairly common (11% and 5% of men). CONCLUSIONS: This analysis provides an empirical approach to examining the presentation of multiple LUTS and suggests it is possible to identify subgroups of patients with LUTS based on their symptom presentation. These analyses need to be replicated to evaluate the clinical relevance of these findings.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".