Translation and Linguistic Validation of the Korean Version of the Dysfunctional Voiding Symptom Score
Bibliographic record
Abstract
Although studies on pediatric dysfunctional voiding are increasing, there have not been enough efforts to validate the Dysfunctional Voiding Symptom Score (DVSS) questionnaire. Therefore, we aimed to translate and validate the DVSS into Korean. The DVSS questionnaire was validated between January and October, 2013. Two bilinguals independently translated the English version of the DVSS questionnaire into Korean, and then reconciled the forward translation of the Korean version. The original DVSS was back-translated into English, then assessed for equivalence to the original. Cognitive debriefing interviews with 5 patients to test the interpretation of the translation were made, then modified and distributed to 48 patients for re-evaluation. A statistical analysis of inter-scale correlation, and test re-test consistency was performed with the Cronbach's alpha coefficient. The changes from patient interviews were reflected in the final version. In an intra-class correlation, the Cronbach's alpha was high in all of the questions (0.97, P < 0.001). Test re-test Cronbach's alpha analysis of reproducibility was higher than 0.8 for all of the 10 questions (P < 0.001). Translation and linguistic validation of Korean version of the DVSS questionnaire was completed by a proper process, with high reliability and validity.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".