Radiographic Evaluation of Femoral Torsion and Correlation With Computed Tomographic Techniques in Labrador Retrievers With and Without Cranial Cruciate Ligament Disease
Bibliographic record
Abstract
OBJECTIVES: To (1) develop a technique to determine the anteversion angle (AA) of the femur on a single radiograph; (2) determine the correlation between this technique and other published radiographic and computed tomographic (CT) methods; and (3) compare the diagnostic outcome of these methods in determining the level at which femoral torsion occurred in Labrador Retrievers with cranial cruciate ligament (CCL) deficiency. STUDY DESIGN: Cross-sectional clinical study. ANIMALS: Mature pure-bred Labrador Retrievers (n = 30). METHODS: Pelvic limbs (n = 28) of 14 dogs without CCL deficiency were classified as control, whereas limbs of 16 dogs (18 limbs) with CCL deficiency were considered as diseased. Femoral torsion was evaluated using radiography and CT and variables were compared among limb groups by use of a mixed-model ANOVA, with P < .05 considered significant. RESULTS: There was a significant association between biplanar and lateral plane AAs but neither correlated with CT assessment of femoral torsion. On CT, a significant correlation was identified between overall AA and each of the distal, proximal, and femoral head trochanteric angles. Biplanar and lateral plane AAs did not differ between normal and CCL deficient limbs. On CT, overall and distal AAs were increased in CCL deficient limbs compared to control. CONCLUSION: Biplanar determination of femoral torsion can be estimated based on a single lateral radiograph but the results will be inaccurate as only CT identified and localized the site of femoral torsion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".