Effect of elbow position on radiographic measurements of radio-capitellar alignment
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Bibliographic record
Abstract
AIM: To evaluate the effect of different elbow and forearm positions on radiocapitellar alignment. METHODS: Fifty-one healthy volunteers were recruited and bilateral elbow radiographs were taken to form a radiologic database. Lateral elbow radiographs were taken with the elbow in five different positions: Maximal extension and forearm in neutral, maximal flexion and forearm in neutral, elbow at 90° and forearm in neutral, elbow at 90° and forearm in supination and elbow at 90° and forearm in pronation. A goniometer was used to verify the accuracy of the elbow's position for the radiographs at a 90° angle. The radiocapitellar ratio (RCR) measurements were then taken on the collected radiographs using the SliceOmatic software. An orthopedic resident performed the radiographic measurements on the 102 elbows, for a total of 510 lateral elbow radiographic measures. ANOVA paired t-tests and Pearson coefficients were used to assess the differences and correlations between the RCR in each position. RESULTS: Mean RCR values were -2% ± 7% (maximal extension), -5% ± 9% (maximal flexion), and for elbow at 90° and forearm in neutral -2% ± 5%, supination 1% ± 6% and pronation 1% ± 5%. ANOVA analyses demonstrated significant differences between the RCR in different elbow and forearm positions. Paired t-tests confirmed significant differences between the RCR at maximal flexion and flexion at 90°, and maximal extension and flexion. The Pearson coefficient showed significant correlations between the RCR with the elbow at 90° - maximal flexion; the forearm in neutral-supination; the forearm in neutral-pronation. CONCLUSION: Overall, 95% of the RCR values are included in the normal range (obtained at 90° of flexion) and a value outside this range, in any position, should raise suspicion for instability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it