Quantification and Reliability of Hip Internal Rotation and the FADIR Test in Supine Position Using a Smartphone Application in an Asymptomatic Population
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to quantify and report the intrarater and interrater reliability of hip internal rotation (IR) range of motion supine with the hip and knee at 90° of flexion and for the flexion-adduction-internal rotation (FADIR) test. Hip internal rotation measured in a lying supine position with the hip and knee at 90° of flexion revealed information on hip impairments. To date no simple quantification approach has been presented in this position; therefore, the FADIR test has not been quantified yet. METHODS: Twenty participants (mean ± standard deviation [SD] age, 24.0 ± 2.1 years; 10 women and 10 men) without lower-limb or back pain were recruited. Three raters evaluated each participant during 2 testing sessions, 1 day apart. A built-in smartphone compass application was used to obtain the hip IR range of motion in both procedures. RESULTS: Mean (± SD) supine IR was 51.7° (± 9.7°) and 62.6° (± 11.4°) for men and women, respectively. Concerning the FADIR test, mean values were 41.8° (± 9.64°) and 50.1° (± 8.0°) for men and women, respectively. The mean intrarater and interrater reliability coefficients were 0.80 and 0.72 for hip IR and 0.75 and 0.40 for the FADIR test. The standard error of the mean ranged from 4.8° to 8.3° (minimal detectable difference [MDD], 13.3° to 22.9°) for hip IR and from 4.6° to 10.3° (MDD, 12.8° to 28.6°) for the FADIR test. CONCLUSION: Overall, the smartphone compass application is adequate to quantify hip IR in a lying supine position. However, the poor to moderate interrater reliability in the FADIR test and the size of the MDD values suggest that the FADIR test should be standardized.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".