Validation of the Questionnaire to Identify Knee Symptoms (QuIKS) using Rasch analysis
Bibliographic record
Abstract
BACKGROUND: The Questionnaire to Identify Knee Symptoms (QuIKS) was recently developed to promote activity by screening for experiences related to early symptoms in people with emergent chronic knee pain problems, such as osteoarthritis (OA) - like knee pain. The main purpose of the current study was to evaluate measurement properties of the QuIKS using Rasch analysis in a sample of people with knee symptoms consistent with symptomatic knee OA. METHOD: This study used cross-sectional data. The sample was 200 subjects along the following knee health continuum: pain-free healthy knees (n = 55) from a university community, knee pain with no knee OA diagnosis (n = 111) from a university-affiliated medical clinic, and patients with surgeon-diagnosed symptomatic knee OA awaiting high tibial osteotomy (n = 34) from a sports medicine surgical clinic. The 13-item QuIKS was evaluated for its factor structure, item- and person-fit, item's category response structure, differential item functioning by sex and obesity status, local item dependency, unidimensionality, and test precision. Subsequently, the QuIKS underwent known-groups analysis and convergent validity with the Knee injury and Osteoarthritis Outcome Score (KOOS). RESULTS: In the QuIKS, each item's category response structure was modified. No differential item functioning was observed. Local item dependency informed the formation of four testlets. This refined QuIKS obtained summary fit to the Rasch measurement model, unidimensionality, reliability (person separation index = 0.82), and interval-level scoring. Subsequently, the Rasch-validated QuIKS (QuIKS-R) demonstrated excellent known-groups validity and good convergent validity with the KOOS (Spearman's rho = 0.45 to 0.77). CONCLUSIONS: The QuIKS-R provides interval-level quantification of knee symptoms-related experiences in people with knee symptoms consistent with symptomatic knee OA. Its scores might be useful for clinicians for promoting activity in individuals with early symptoms consistent with symptomatic knee OA.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".