Appropriate questionnaires for knee arthroplasty
Bibliographic record
Abstract
The Swedish Knee Arthroplasty Registry (SKAR) has recorded knee arthroplasties prospectively in Sweden since 1975. The only outcome measure available to date has been revision status. While questionnaires on health outcome may function as more comprehensive endpoints, it is unclear which are the most appropriate. We tested various outcome questionnaires in order to determine which is the best for patients who have had knee arthroplasty as applied in a cross-sectional, discriminative, postal survey. Four general health questionnaires (NHP, SF-12, SF-36 and SIP) and three disease/site-specific questionnaires (Lequesne, Oxford-12, and WOMAC) were tested on 3600 patients randomly selected from the SKAR. Differences were found between questionnaires in response rate, time required for completion, the need for assistance, the efficiency of completion, the validity of the content and the reliability. The mean overall ranks for each questionnaire were generated. The SF-12 ranked the best for the general health, and the Oxford-12 for the disease/site-specific questionnaires. These two questionnaires could therefore be recommended as the most appropriate for use with a large knee arthroplasty database in a cross-sectional population.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.048 |
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".