A Systematic Review and Quality Assessment of Cardiovascular Disease-Specific Health-Related Quality-of-Life Instruments Part I: Instrument Development and Content Validity
Bibliographic record
Abstract
OBJECTIVES: Health-related quality-of-life (HRQoL) instruments for cardiovascular diseases (CVD) have been commonly used to measure important patient-reported outcomes (PROs) in clinical trials and practices. This study aimed at systematically identifying and assessing the content validity of CVD-specific HRQoL instruments in clinical studies. METHODS: The research team searched Cumulative Index to Nursing and Allied Health Literature, Embase, and PubMed from inception to January 20, 2022. The research team included studies that reported the development and content validity for CVD-specific instruments. Two reviewers independently assessed the methodological quality using the Consensus-based Standards for the Selection of Health Measurement Instruments methods on evaluating content validity of PROs. Content analysis was used to categorize the items included in the instruments. RESULTS: The research team found 69 studies reporting the content validity of 40 instruments specifically developed for CVD. Fourteen (35.0%) were rated "sufficient" with very low to moderate quality of evidence. For PRO development, all instruments were rated "doubtful" or "inadequate." Twenty-eight (70.0%) instruments cover the core concepts of HRQoL. CONCLUSIONS: The quality of development and content validity vary among existing CVD-specific instruments. The evidence on the content validity should be considered when choosing a HRQoL instrument in CVD clinical studies and health economic evaluations.
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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 | 0.080 | 0.311 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".