Quantification of measurement of femoral head coverage and Norberg angle within and among four breeds of dogs
Bibliographic record
Abstract
OBJECTIVE: To report values for percentage coverage of the femoral head (PC) and Norberg angle (NA) in 4 common breeds of dogs and to determine values for each that distinguish between normal and dysplastic hip status on the basis of Orthopedic Foundation for Animals (OFA) hip evaluation. ANIMALS: 1,841 dogs 24 to 48 months of age that were Labrador Retrievers (455), Golden Retrievers (423), Rottweilers (545), or German Shepherd Dogs (418). PROCEDURE: Retrospective analysis of NA and PC measured from standard OFA ventrodorsal pelvic radiographs from 4 breeds of dog. RESULTS: Norberg angle ranged from 67.4 to 124.4 degrees for Labrador Retrievers, 59.7 to 128.6 degrees for Rottweilers, 70.2 to 119.4 degrees for Golden Retrievers, and 55.3 to 121.3 degrees for German Shepherd Dogs. The PC ranged from 6.5 to 79.9% for Labrador Retrievers, 5.7 to 79.5% for Rottweilers, 8.3 to 79.3% for Golden Retrievers, and 5.4 to 83.7% for German Shepherd Dogs. On the basis of logistic regression modeling for determining normal versus abnormal hip status for all 4 breeds, cutoff points for NA were <105 degrees and PC were <50%. CONCLUSIONS AND CLINICAL RELEVANCE: Results of our study indicate that cutoff points of NA of 105 degrees and PC of 50% do not differentiate normal versus dysplastic hip status. Each of the 4 breeds had different values for NA and PC that distinguished normal from dysplastic hip status.
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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) | 0.000 | 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".