Preliminary validation of the Claudication Symptom Instrument (CSI)
Bibliographic record
Abstract
This article describes the development of the Claudication Symptom Instrument (CSI) and its measurement properties for evaluating the symptom experience of patients diagnosed with intermittent claudication (IC). We conducted semi-structured qualitative interviews with IC patients for item development and cognitive interviews in which patient comprehension of items was tested. We evaluated measurement properties using data collected and analyzed in the context of an observational comparative effectiveness study of IC treatments. Items measuring five symptom important to patients were developed and cognitively tested: Pain, Numbness, Heaviness, Cramping, and Tingling. Item means (higher means worse) ranged from 1.1 (Tingling) to 2.3 (Pain) (range: 0 ‘none’ to 4 ‘extreme’). Rasch analysis yielded support for an overall score (χ 2 =26.5, df=20, p=0.15). The total CSI score differed by clinician-rated severity of mild versus moderate ( p<0.05), but not moderate versus severe. Re-administration of the CSI 5–10 days after baseline yielded an intra-class correlation coefficient of 0.86. Changes in CSI total score and VASCUQOL total score between baseline and 6 months post-treatment were correlated at −0.52 ( p<0.05). The CSI preliminarily meets accepted measurement standards for content validity, internal consistency and test-retest reliability, construct validity, and sensitivity for detecting change. Because of its high test-retest reliability, it may also be useful in clinical care with individual patients. It takes approximately 3 minutes to complete.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".