Assessment of the hip reduction angle for predicting osteoarthritis of the hip in the Labrador Retriever
Bibliographic record
Abstract
Hip palpation has been used to provide semiquantitative information regarding passive joint laxity and susceptibility to hip dysplasia. The purpose of this study was to: (1) evaluate the intra- and inter-examiner repeatability of the hip reduction angle measured at 4 months of age by three examiners using manual goniometry and an electromagnetic tracking system; (2) compare the hip reduction angle measured with manual goniometry to the hip reduction angle measured with the electromagnetic tracking system; and (3) evaluate the hip reduction angle, distraction index and Ortolani manoeuvre at 4 months of age as predictors of the development of hip osteoarthritis at 12 months of age in 11 Labrador Retriever dogs. Intra- and inter-examiner repeatability was demonstrated for both the manual and electromagnetic goniometric measurement of the hip reduction angle (coefficient of variation < 4.3% and < 6.1%; and P = 0.163 and P = 0.836 respectively). The hip reduction angle measured by manual goniometry was moderately correlated to the hip reduction angle measured by the electromagnetic tracking system (r = 0.603, P < 0.0000). The hip reduction angle measured by manual and electromagnetic goniometry was a poor predictor of osteoarthritis at 12 months of age (r = 0.231, P < 0.062, and r = 0.321, P < 0.01). The distraction index was moderately correlated with the development of osteoarthritis by 12 months of age (r = 0.493, P < 0.0000). The Ortolani sign was sensitive (100%) but not specific (41%) for the development of osteoarthritis at 12 months of age. The hip reduction angle did not further quantify the Ortolani manoeuvre as a predictor of osteoarthritis in 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 | 0.003 |
| 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) | 0.000 | 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".