Validation of the Japanese Version of The Wisconsin Stone Quality of Life Questionnaire: Results from SMART Study Group
Bibliographic record
Abstract
Background: The Wisconsin Stone Quality of Life questionnaire (WISQOL) is a health-related quality of life (HRQOL) measure designed for patients with urinary stones. It has been translated and used in several languages. This study aimed to validate the Japanese version of the WISQOL (J-WISQOL). Materials and Methods: The J-WISQOL was translated and validated using a multistep process proposed by the World Health Organization that involved forward translation, back-translation, and pilot testing with a group of patients. This study enrolled 150 patients with urinary stones who visited three academic hospitals for stone treatment. We assessed convergent validity of correlation patterns and internal consistency of the J-WISQOL and Short-Form 36-item survey version 2 (SF-36v2). Results: Overall, 150 patients were enrolled. The mean total score of the J-WISQOL was 108.18 ± 20.26 (raw score min–max, 28–140), suggesting that the onset and symptoms of urinary stones reduced the HRQOL in the patients. The J-WISQOL showed good internal consistency (Cronbach's α = 0.96) and interdomain associations (Spearman's correlation coefficient r = 0.67–0.94). The J-WISQOL was correlated with the SF-36v2 in all domains: social, emotional, health, and vitality impact (r = 0.47–0.66). Conclusion: The J-WISQOL is a reliable instrument for evaluating HRQOL measures in patients with urinary stones. It could be a useful quality of life questionnaire for urinary stones in Japan. Clinical Trial 60-20-0047.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".