Further Clinical Validation of the Walking Impairment Questionnaire for Classification of Walking Performance in Patients with Peripheral Artery Disease
Bibliographic record
Abstract
The purpose of this study was to further validate the Walking Impairment Questionnaire (WIQ) as a self-report tool to aid in the clinical identification of walking ability of patients with peripheral artery disease (PAD). 132 patients with PAD and an ankle brachial index (ABI) ≤0.90 were enrolled; 123 provided complete data for the WIQ and standardized graded treadmill test. The WIQ scores were consistent with reported scores in other studies. The absolute claudication distance (ACD) ranged from 42.3 to 1589.2 meters; the peak walking time (PWT) ranged from 68 to 1800 seconds. Adjusted WIQ scores were positively and moderately associated with the log transformed ACD and PWT (r > .53, P < .001). Based on the area under the curve analysis, an overall WIQ score of 42.5 or less identified low performers (sensitivity 0.90, specificity 0.73); the combined subscale score of distance and stair of 75.5 or more identified high performers (sensitivity 0.41, specificity 0.90). We conclude that WIQ cut-offs appropriately classify walking performance in PAD patients, making this a potentially useful clinical tool. Consideration needs to be given to incorporating a standardized WIQ version into practice guidelines and the use of innovative strategies to facilitate clinical uptake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".