Factors contributing to the variability of a predictive score for cranial cruciate ligament deficiency in Labrador Retrievers
Bibliographic record
Abstract
BACKGROUND: We recently reported that a conformation score derived from the tibial plateau angle (TPA) and the femoral anteversion angle (FAA), best discriminates limbs predisposed to, or affected by cranial cruciate ligament disease (CCLD), from those that are at low risk for CCLD. The specificity and sensitivity of this score were high enough to support further investigations toward its use for large-scale screening of dogs by veterinarians. The next step, which is the objective of the current study, is to determine inter-observer variability of that CCLD score in a large population of Labrador Retrievers. A total of 167 Labradors were enrolled in this cross-sectional study. Limbs of normal dogs over 6 years of age with no history of CCLD were considered at low risk for CCLD. Limbs of dogs with CCLD were considered at high risk for CCLD. Tibial plateau and femoral anteversion angles were measured independently by two investigators to calculate a CCLD score for each limb. Kappa statistics were used to determine the extent of agreement between investigators. Pearson's correlation and intraclass coefficients were calculated to evaluate the correlation between investigators and the relative contribution of each measurement to the variability of the CCLD score. RESULTS: The correlation between CCLD scores calculated by investigators was good (correlation coefficient = 0.68 p < 0.0001). However, interobserver agreement with regards to the predicted status of limbs was fair (kappa value = 0.28), with 37% of limbs being assigned divergent classifications. Variations in CCLD scores correlated best with those of TPA, which was the least consistent parameter between investigators. Absolute interobserver differences were two times greater for FAAs (4.19° ± 3.15) than TPAs (2.23° ± 1.91). CONCLUSIONS: The reproducibility of the CCLD score between investigators is fair, justifying caution when interpreting individual scores. Future studies should focus on improving the reproducibility of TPA and FAA measurements, as strategies to improve the agreement between CCLD scores.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".