Questionnaire to Identify Knee Symptoms: Development of a Tool to Identify Early Experiences Consistent With Knee Osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Current diagnostic procedures for knee osteoarthritis (OA) identify individuals late in the disease process. A questionnaire may be a useful and inexpensive method to screen for early symptoms of knee OA. OBJECTIVE: The purpose of this study was to develop a brief, self-administered questionnaire for clinical and research settings to identify emerging knee problems in people who could benefit from conservative interventions. DESIGN: This prospective study utilized a mixed-methods approach. METHODS AND RESULTS: Questionnaire items were generated from interview data from individuals with emerging chronic knee problems. These items were reviewed by 16 rheumatology experts, resulting in a 35-item draft questionnaire. To reduce the number of items, questionnaires were mailed to 228 adults, aged 40 to 65 years, with evidence of ongoing knee problems. One hundred thirteen completed questionnaires were returned (63.1% response rate), with 105 usable questionnaires. Using principal components analysis, the number of items was reduced to a final 13-item version, the Questionnaire to Identify Knee Symptoms (QuIKS). The QuIKS has 4 subscales: medications, monitoring, interpreting, and modifying. The QuIKS demonstrated strong internal consistency. LIMITATIONS: A sampling bias among respondents who provided data for item reduction likely means that the QuIKS reflects those who self-report knee problems to a health care provider, which may not be generalizable to the population. CONCLUSIONS: The QuIKS is a short, self-administered questionnaire used to promote activity by identifying the experiences associated with early symptoms consistent with knee OA, such as monitoring intermittent symptoms, interpreting the meaning of these symptoms, modifying behaviors, and including the use of medications. If future work validates the QuIKS, its use in developing samples could expand our understanding of early disease and improve interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".