Does malpositioning of the arm influence radiographic range of motion measurement?
Bibliographic record
Abstract
PURPOSE: Radiographic range of motion measurement of the elbow has been shown to be both precise and reliable. For this method to be used routinely in research studies, it is important to describe its limits regarding: (1) rotation of the arm from the perfect lateral position and (2) the length of humerus and ulna visible on the radiograph. MATERIAL AND METHODS: A 3D bone reconstruction was performed from an upper limb CT scan. Planar radiographs were simulated for rotations of the elbow within a range of ±30o from the perfect lateral position. The field of view was modified, ranging from five visible centimeters of diaphysis on the radiograph to full visibility of the upper limb. RESULTS: The disparity was less than 2.5° (mean=0.68°, SD=0.43°) when the flexed arm was rotated between -30.0° (external rotation, ER) and + 18.0° (internal rotation, IR). When considering the extended arm, measured angles differed by less than 2.5° (mean=0.79°, SD=0.57°) within a range of -15.0° (ER) to +30.0° (IR). When a minimum of 12 cm of humerus and ulna, from the capitellum, were visible on the radiograph measured angles varied very slightly (mean disparity of 0.71°, SD= 0.71°). Finally a qualitative description of the appearance of the radiographs was included to help surgeons estimate acceptable degrees of rotation. CONCLUSION: Range of motion (ROM) measurement shows consistent results, despite 15 to 30 degrees of internal or external rotation. The middle third of the humeral and ulnar diaphyses should be visible on the radiographs to ensure the validity of measurement. Radiographic ROM measurement is still recommended over the goniometer for research purposes because of its high reliability and precision. Moreover, malpositioning of the elbow should not jeopardize results since it will most likely be an angle measurement variation of less than 2.5°.
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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 | 0.013 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".