Thoracic Limb Alignment in Healthy Labrador Retrievers: Evaluation of Standing Versus Recumbent Frontal Plane Radiography
Bibliographic record
Abstract
OBJECTIVE: To report thoracic limb alignment values in healthy dogs; to determine if limb alignment values are significantly different when obtained from standing versus recumbent radiographic projections. STUDY DESIGN: Prospective cross-sectional study. ANIMALS: Labrador Retrievers (n = 45) >15 months of age. METHODS: Standing and recumbent radiographs were obtained and limb montages were randomized before analysis by a single investigator blinded to dog, limb, and limb position. Twelve limb alignment values were determined using the CORA methodology. Measurements were performed in triplicate and intra-observer variability was evaluated by intra-class correlation coefficient (ICC). Limb alignment values were reported as mean ± SD and 95% confidence intervals. Linear mixed models were used to determine if significant associations existed between limb alignment values and limb, limb position, gender, age, weight, and body condition score. RESULTS: There were significant differences in standing and recumbent limb alignment values for all values except elbow mechanical axis deviation (eMAD). Limb, gender, age, body weight, and body condition score had no effect. ICC values ranged from 0.522 to 0.758, indicating moderate to substantial agreement for repeated measurements by a single investigator. CONCLUSIONS: Limb alignment values are significantly different when determined from standing versus recumbent radiographs in healthy Labrador Retrievers.
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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 | 0.002 |
| 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 | 0.000 |
| 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".