Validity of Goniometric Elbow Measurements: Comparative Study with a Radiographic Method
Bibliographic record
Abstract
BACKGROUND: A universal goniometer is commonly used to measure the elbow's ROM and carrying angle; however, some authors question its poor intertester reliability. QUESTIONS/PURPOSES: We (1) assessed the validity of goniometric measurements as compared with radiographic measurements in the evaluation of ROM of the elbow and (2) determined the reliability of both. METHODS: The ROM and carrying angle of 51 healthy subjects (102 elbows) were measured using two methods: with a universal goniometer by one observer three times and on radiographs by two independent examiners. Paired t-test and Pearson's correlation were used to compare and detect the relationship between mean ROM. The maximal error was calculated according to the Bland and Altman method. RESULTS: The intraclass correlation coefficients (ICC) ranged from 0.945 to 0.973 for the goniometric measurements and from 0.980 to 0.991 for the radiographic measurements. The two methods correlated when measuring the total ROM in flexion and extension. The maximal errors of the goniometric measurement were 10.3° for extension, 7.0° for flexion, and 6.5° for carrying angle 95% of the time. We observed differences for maximum flexion, maximal extension, and carrying angle between the methods. CONCLUSION: Both measurement methods differ but they correlate. When measured with a goniometer, the elbow ROM shows a maximal error of approximately 10°. CLINICAL RELEVANCE: The goniometer is a reasonable and simple clinical tool, but for research protocols, we suggest using the radiographic method because of the higher level of precision required.
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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 | 0.027 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".