Patient-reported outcome measures in patients with peripheral arterial disease: a systematic review of psychometric properties
Bibliographic record
Abstract
BACKGROUND: Peripheral arterial disease (PAD) is generally associated with considerable morbidity and reduced quality of life. Patient-reported outcome measures (PROMs) provide important information about the burden of disease and impact of treatment in affected patients. OBJECTIVES: The objective of the review was to identify and appraise studies reporting the psychometric evaluation of PROMs administered to a specified population of patients with PAD with a view to recommending suitable PROMs. METHODS: A systematic review of peer-reviewed English language articles was undertaken to identify primary studies reporting psychometric properties of PROMs in English-speaking patients with various stages of PAD. Comprehensive searches were completed up until January 2015. Study selection, data extraction and quality assessment were undertaken independently by at least two researchers. Findings were presented as tabular and narrative summaries based on accepted guidance. RESULTS: Psychometric evaluation of 6 generic and 7 condition-specific PROMs reported in 14 studies contributed data to the review. The frequently reported measure was the SF-36 (n = 11 studies); others included the Walking Impairment Questionnaire (n = 8 studies), EQ-5D (n = 5 studies) and the Vascular Quality of Life Questionnaire (n = 3 studies). Studies included a diverse PAD population and varied in methodology, including approach to validation of PROMs. CONCLUSIONS: Various PROMs have been validated in patients with PAD but no study provided evidence of a full psychometric evaluation in the patient population. Careful selection is required to identify reliable and valid PROMs to use in clinical and research settings.
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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 | 0.040 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".