Cultural adaptation and validation of the Chinese version of the expanded prostate cancer index composite
Bibliographic record
Abstract
AIM: The expanded prostate cancer index composite (EPIC) is a widely adopted instrument for the quality of life of patients with prostate cancer. We aimed to create a Chinese version of EPIC to further research in the Chinese-speaking population. METHODS: A prototype was created by forward-backward translations and revisions. During cultural adaptation, 15 participants were interviewed after they had completed the prototype. A few issues highlighted included confusion related to the question format, subject non-familiarity with the Chinese term for "hot flashes," and the use of the Chinese term for "breast" as a strictly female body part. A pilot version was created based on the cultural adaptation findings. Validation of the pilot version was performed by having 50 participants complete the Chinese EPIC and EORTC QLQ-c30 twice within a 4-week period. Test-retest reliability (Pearson's correlations and difference distribution) and internal consistency (Cronbach's α) were measured using SAS version 9.4. RESULTS: Test-retest reliability values for the urinary, bowel, sexual and hormone domains were 0.71, 0.51, 0.51 and 0.66, respectively; subscale test-retest reliability ranged between 0.29 and 0.82. Internal consistency for domains was good with Cronbach's α ranging from 0.76 to 0.78 for the initial test and 0.67 to 0.85 for the retest. The performance of this version of EPIC was comparable to the validated EORTC QLQ-C30. CONCLUSION: The EPIC questionnaire was successfully translated into Chinese and was culturally adapted. The resultant Chinese version has high reliability and validity and will be an important tool for research on quality of life in the Chinese population.
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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 | 0.015 | 0.020 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".