A brief screening tool for knee pain in primary care. 1. Validity and reliability
Bibliographic record
Abstract
OBJECTIVES: To design and test the performance of a new knee pain screening tool (KNEST), both separately and together with a combination of existing questionnaires, which will be used to assess the general health status of knee pain sufferers in primary care. METHODS: A postal survey of knee pain and disability was sent to a random sample of 240 individuals aged over 55 yr registered with two general practices in North STAFFORDSHIRE: The survey questionnaire consisted of the KNEST; a pain manikin; the Short Form 36 (SF-36); the Hospital Anxiety and Depression Scale (HADS); demographic questions; and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for those who reported knee pain. A second, identical questionnaire was sent 2 weeks later to a random subsample of responders (n=80) to test repeatability. RESULTS: An 85% baseline response rate was achieved for the first questionnaire. The 12-month prevalence of knee pain identified from baseline responders to the survey was 45%. A response rate of 74% was achieved for the repeatability questionnaire. Each section of the questionnaire was well completed and repeatability was good for nearly all measures (most reliability scores exceeded 0.6). A new core question about knee pain showed good internal reliability, with an agreement score of 91% between baseline and retest assessment, and good construct validity in relation to knee pain identified on the pain manikin (agreement 95%). Good agreement was found between recalled consultation for knee pain in the questionnaire and evidence of consultation for knee pain in general practice records. CONCLUSIONS: The KNEST appears to be a reliable and valid composite tool for the study of population needs and outcomes of care for people aged over 55 yr with knee pain.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".