Effect of elbow position on radiographic measurements of radio-capitellar alignment
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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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".