Effect of Limb Position on Elbow Congruity with CT Evaluation
Bibliographic record
Abstract
OBJECTIVE: To determine if computed tomographic (CT) measurement of radio-ulnar congruence (RUC) is affected by limb position. STUDY DESIGN: Prospective study. ANIMALS: Labrador Retrievers (n=10). METHODS: Each dog had both elbows imaged in neutral, supination, and pronation positions using helical CT acquisition, at 1 mm increments, in a transverse plane. RUC was calculated using a previously reported method and a new technique using 3-D image processing and measurements determined by a coordinate system. RESULTS: The limits of agreement showed that comparing any pair of elbow positions yielded large discrepancies in measurements with the previously reported technique. Better agreement between pairs of elbow positions with reduced discrepancy was achieved using the 3-D technique. Variation in antebrachium positioning did yield a difference in congruence. CONCLUSIONS: Limb position (supination, pronation) affects elbow congruity measurements using CT analysis. Use of 3-D image processing may allow for improved elbow congruity measurements compared with other 2-D measurement techniques. CLINICAL RELEVANCE: Limb position must be controlled when performing CT evaluation of elbows for incongruity. 3-D imaging appears to be less affected by limb position than conventional 2-D analysis when assessing radioulnar incongruence.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".