Apparent Proximal Ulna Dorsal Angulation Variation Due to Ulnar Rotation
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of ulna rotation on the apparent proximal ulna dorsal angulation (PUDA). METHODS: Computed tomography images of 59 ulnas were included in this study, 48 being bilateral specimens and the remaining 11 were unilateral. Three-dimensional models of the entire ulna were obtained, and the ulnas were rotated in 5-degree increments in internal rotation or external rotation from neutral. PUDA, PUDA apex, varus angulation, and varus apex were measured on each ulna. RESULTS: With the ulna in neutral rotation, the mean (95% CI) PUDA was 3.7 (2.9-4.5) degrees, whereas the mean varus angle was 10.5 (9.8-11.1) degrees. The varus angle apex and PUDA apex were 28.9 (27.5-30.2)% and 19.6 (18.7-20.6)% along the total length of the ulna, respectively. As the ulna was rotated externally by 5, 10, and 15 degrees, the PUDA increased by 0.7 (0.5-0.9) degrees, 1.2 (0.9-1.4) degrees, and 1.4 (1.1-1.8) degrees, respectively. Conversely, with internal rotation of 5, 10, and 15 degrees, the PUDA decreased by 0.9 (0.8-1.1) degrees, 2.0 (1.8-2.3) degrees, and 3.3 (2.7-3.9) degrees, respectively. CONCLUSIONS: This study demonstrates that small degrees of ulna rotation result in a statistically significant change in the apparent PUDA; however, this may not represent a clinically significant difference. Because of the anatomic variation between patients, it is important to obtain a contralateral film to determine the PUDA for anatomic reduction of the ulna in complex cases. When using a contralateral image, it is important to obtain a true lateral film or consider using 3-dimensional imaging for preoperative planning.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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 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".