Analysis of a Laxity Index Database and Comparison with the Fédération Cynologique Internationale Grades of This Population
Bibliographic record
Abstract
Abstract Objective This study aimed to analyse the distribution of the laxity indices (LI) in a dog population, to compare the LI with the Fédération Cynologique Internationale (FCI) grades and to search for differences of LI between breeds. Study Design The database was composed of all dogs presented to the University Hospital of the Faculty of Veterinary Medicine in Ghent for obligatory hip screening between January 2016 and February 2019, and all patients presented to orthopaedic consultation between January 2017 and January 2019 for a complaint of hindlimb lameness, which underwent both a standard extended ventrodorsal radiograph of the hips and a stress radiograph revealing hip joint laxity. The latter was obtained by means of the Vezzoni-modified Badertscher distension device and the LI was calculated. For each dog of the population, the LI was then compared with the FCI grade. Results The LI values ranged between 0.15 and 1.04, with a mean of 0.46. The LI and the FCI grade increased together, and showed a moderate-to-good correlation. There was a highly significant overall difference in the mean value of LI per FCI grade group (p < 0.001). The mean LI of the Labrador Retrievers was slightly but significantly lower than the mean LI of the Golden Retrievers (p < 0.01). Conclusion The LI calculated on a stress radiograph taken with the Vezzoni-modified Badertscher distension device shows a good correlation with the FCI grade assigned on a standard extended ventrodorsal projection. A wide range of passive hip joint laxity exists in dogs considered to be phenotypically normal based on the FCI grading method.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".