Smartphone Inclinometry Is a Valid and Reliable Tool for Measuring Frontal Plane Tibial Alignment in Healthy and Osteoarthritic Knees
Bibliographic record
Abstract
OBJECTIVE: Frontal plane knee alignment plays an integral role in tibiofemoral knee osteoarthritis development and progression. Accessible methods for obtaining direct or indirect measures of knee alignment may help inform clinical decision making when specialized equipment is unavailable. The present study evaluated the concurrent validity, as well as intersession (within-rater) and interrater (within-session) reliability of smartphone inclinometry for measuring static frontal plane tibial alignment-a known proxy of frontal plane knee alignment. METHODS: Twenty healthy individuals and 38 patients with knee osteoarthritis were measured for frontal plane tibial alignment by a pair of raters using smartphone inclinometry, manual inclinometry, and 3-dimensional motion capture simultaneously. Healthy participants were measured on 2 separate days. Bland-Altman analysis, supplemented with intraclass correlation coefficient (ICC)(2,k), was used to assess concurrent validity. ICC(2,k), SEM, and minimum detectable change with 95% confidence limits (MDC95) were used to assess measurement reliability. RESULTS: Compared against motion capture, smartphone inclinometry measured frontal plane tibial alignment with a mean difference of 0.7 and 1.1 degrees (biased toward varus) for healthy participants and participants with knee osteoarthritis, respectively (ICC[2,k] ≥ 0.87). Smartphone inclinometry measurements demonstrated adequate intersession (within-rater) relative (ICC[2,k] = 0.91) and absolute (SEM = 0.7 degrees; MDC95 = 1.8 degrees) reliability, which outperformed manual inclinometry (ICC[2,k] = 0.85; SEM = 1.0 degrees; MDC95 = 2.6 degrees). Interrater (within-session) reliability of smartphone inclinometry was acceptable in both cohorts (ICC[2,k] = 0.93; SEM = 0.4 degrees to 1.2 degrees; MDC95 = 1.2 degrees to 3.2 degrees). CONCLUSION: Smartphone inclinometry is sufficiently valid and reliable for measuring frontal plane tibial alignment in healthy individuals and patients with medial tibiofemoral knee osteoarthritis. IMPACT: Smartphones are readily accessible by clinicians and researchers. Our assessment of measurement validity and reliability supports the use of smartphone inclinometry as a clinically available tool to measure frontal plane tibial alignment without medical imaging or specialized equipment.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".