Agreement Among Paper and Electronic Modes of the EQ-5D-5L
Bibliographic record
Abstract
INTRODUCTION: While the EQ-5D-5L has been migrated to several electronic modes, evidence supporting the measurement equivalence of the original paper-based instrument to the electronic modes is limited. OBJECTIVES: This study was designed to comprehensively examine the equivalence of the paper and electronic modes (i.e., handheld, tablet, interactive voice response [IVR], and web). METHODS: As part of the foundational work for this study, the test-retest reliability of the paper-based, UK English format of the EQ-5D-5L was assessed using a single-group, single-visit, two-period, repeated-measures design. To compare paper and electronic modes, three independent samples were recruited into a three-period crossover study. Each participant was assigned to one of six groups to account for order effects. Descriptive statistics, mean differences (i.e., split-plot analysis of variance [ANOVA]), and intraclass correlation coefficients (ICCs) were calculated. RESULTS: The test-retest results showed mean differences near zero and ICC values > 0.90 for both the index and the EQ VAS scores. For the electronic comparisons, mean difference confidence intervals (CIs) for the EQ-5D index scores and EQ VAS scores reflected equivalence of the means across all modes, as the CIs were wholly contained inside the equivalence interval. Further, the ICC 95% lower CIs for the index and EQ VAS scores showed values above the thresholds for denoting equivalence across all comparisons in each sample. No significant mode-by-order interactions were present in any ANOVA model. CONCLUSIONS: Overall, our comparisons of the paper, screen-based, and phone-based formats of the EQ-5D-5L provided substantial evidence to support the measurement equivalence of these modes of data collection.
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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 | 0.029 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".