Test–Retest and Interrater Reliability of Goniometric Tibial Rotation Range of Motion Measurements
Bibliographic record
Abstract
Purpose: Although suggested to be an important component of physiologic knee function, the ability to accurately assess tibial rotation range of motion (ROM) is currently limited by a lack of reliability data for clinically practical measurement tools. The purpose of the present study was to estimate the reliability of tibial rotation ROM measurements taken with subjects in sitting and supine positions. Methods: Thirty healthy subjects (mean age 37 ± 12 years) completed maximal active internal and external tibial rotation movements in sitting and supine positions. ROM was assessed on two occasions and by two raters using a gravity- and magnetic-referenced goniometer. Results: There were significant differences between internal and external rotation ROM values (p < .05) and between values obtained with subjects in sitting and supine positions (p < .05). Intraclass correlation coefficients (ICCs) suggested that test–retest reliability for all measurements was excellent (ICC = 0.83–0.93), whereas interrater reliability was poor to excellent (ICC = 0.39–0.81). Standard errors of measurement ranged from approximately 1 to 2°. Conclusions: These findings suggest that tibial rotation ROM can be assessed reliably by one rater using one test position. However, caution must be adopted when comparing ROM measurements assessed by different raters or in different test positions.
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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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".