The kneeling test is a valid method of assessing kneeling tolerance
Bibliographic record
Abstract
PURPOSE: No quantitative test exists to reliably assess kneeling tolerance before and after surgery. The aim of this study was to validate a kneeling test, designed to quantify kneeling tolerance. METHOD: A total of 179 participants (324 knees) were enrolled into the study, including 124 asymptomatic knees, 98 with osteoarthritis (OA), 85 following total knee arthroplasty (TKA) and 17 following anterior cruciate ligament reconstruction (ACLR). Patients were asked to kneel on a custom-built platform on a soft, firm and hard surface, at both 90° and 110° of knee flexion. A kneeling score of 0-100 was established for 90° and 110° with 100 being a complete absence of pain or discomfort. A linear mixed model with random intercept was used to estimate differences between conditions (healthy, OA, ACLR and TKA). Pearson's correlation coefficient was used to test the strength of the association between the kneeling test and the forgotten joint score (FJS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score. Test-retest reliability was estimated by the intraclass correlation coefficient (ICC). RESULTS: . The kneeling test demonstrated construct validity, with a moderately strong correlation observed between the kneeling test and the FJS and the WOMAC at 90° (FJS 0.474 [95%CI: 0.357, 0.577], WOMAC - 0.503 [- 0.389, - 0.602]) and 110° (FJS 0.579 [95% CI: 0.479, 0.665], WOMAC - 0.648 [- 0.560, - 0.722]). The ICC for the kneeling test at 90° and 110° was 0.843 (95% CI: 0.745, 0.905) and 0.926 (95% CI: 0.877, 0.956), respectively. CONCLUSIONS: The kneeling test is a valid technique to quantitatively determine kneeling tolerance. It can aid in the assessment and modification of current surgical techniques to improve patient outcomes. LEVEL OF EVIDENCE: III.
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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 | 0.005 | 0.015 |
| 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 | 0.001 | 0.000 |
| 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".