The Use of Generic Patient-Reported Outcome Measures in Emergency Department Surveys: Discriminant Validity Evidence for the Veterans RAND 12-Item Health Survey and the EQ-5D
Bibliographic record
Abstract
Objectives This study aimed to compare discriminant validity evidence of 2 generic patient-reported outcome measures (PROMs), the Veterans RAND 12-Item Health Survey (VR-12) and level 5 of EQ-5D (EQ-5D-5L), for use in emergency departments (EDs). Methods Data were obtained via a cross-sectional survey of 5876 patients in British Columbia (Canada) who completed a questionnaire after visiting an ED in 2018. We compared the extent to which the VR-12 and the EQ-5D-5L distinguished among groups of ED patients with different levels of comorbidity burden and self-reported physical and mental or emotional health status. Multivariable logistic regression was used to evaluate the ability of the 2 PROMs to identify patients presenting with a mental health (MH) condition. Results All the measures produced small effect sizes (ESs) for discriminating comorbidity levels (R 2 range: 0.00 [VR-12 mental component summary {MCS}] to 0.10 [VR-12 physical component summary score]). The EQ-5D visual analog scale offered the largest ES for discriminating self-reported physical health (R 2 = 0.48), whereas the MCS, the VR-12 MH domain, and the EQ-5D-5L anxiety/depression dimension had the largest ESs for discriminating self-reported mental or emotional health (R 2 = 0.42, 0.40, and 0.38, respectively). The MCS produced a medium ES (R 2 = 0.42) along with the VR-12 utility score (R 2 = 0.27) compared with the EQ-5D-5L index (R 2 = 0.19). Having a MH condition was predominantly identified by the MCS (Pratt index = 0.56). Conclusions The VR-12 PROM provides a more comprehensive measurement of MH than the EQ-5D-5L, which is important to inform healthcare service needs for patients who present in EDs with MH challenges.
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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 | 0.104 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".