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 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 | 0.003 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".