Evaluating the measurement properties of the 26-item Expanded Prostate Cancer Index Composite (EPIC-26) with a multicenter cohort
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate the psychometric properties of the 26-item Expanded Prostate Cancer Index Composite (EPIC-26) for measuring the quality of life in patients treated for localized prostate cancer. The EPIC-26 is a patient-reported outcome instrument recommended for use with patients treated for localized prostate cancer. METHODS: This study is based on data collected prospectively between September 2014 and February 2017 in Alberta, Canada. Men were treated with either radical prostatectomy or radiation therapy and administered the EPIC-26. Responses to the EPIC-26 were the primary outcome. Construct validity was measured using confirmatory factor analysis. Reliability was measured using Chronbach's alpha and item-total correlation. Ceiling and floor effects were also investigated. RESULTS: EPIC-26 response data from 205 participants (prostatectomy =138; radiation=60; both=7) were used in this analysis. The EPIC-26 was administered an average of 33.8 weeks after treatment. The confirmatory factor analysis model did not meet the threshold for adequate fit. Several items had near-zero factor loadings and were non-significant. Four out of the EPIC-26's five domains met the acceptable reliability threshold based on Cronbach's alpha. Ceiling effects were observed in four out of five domains. CONCLUSIONS: The EPIC-26 demonstrated poor construct validity, adequate reliability, and large ceiling effects. Several issues were observed, suggesting that the instrument's five domains were not well-defined by their respective items. The original EPIC's conceptual framework should be reviewed and the shortened instrument revised to improve its performance for measuring post-treatment quality of life.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".