Comparison of tibial plateau angles in dogs with and without cranial cruciate ligament injuries
Bibliographic record
Abstract
OBJECTIVE: To measure and compare tibial plateau angles (TPA) of dogs with cranial cruciate ligament (CrCL) injuries and dogs without CrCL injuries. DESIGN: Prospective study. ANIMALS: 87 dogs. PROCEDURE: Stifle joints were measured from lateral radiographic views to determine TPA in 3 groups: group-1 dogs had CrCL injuries, group-1a dogs, a subgroup of group 1, had 1 unaffected stifle joint, and group-2 dogs had no CrCL injuries. Age, sex, breed, body weight, limb injured, and TPA were recorded for each dog. RESULTS: 56 stifle joints were measured in group-1 dogs; mean TPA was 23.76 degrees , and mean age and weight were 5.7 years and 37.91 kg (83.4 lb), respectively. Fourteen stifle joints were measured in group-1a dogs; mean TPA was 24.71 degrees , and mean age and weight were 5.6 years and 38.06 kg (83.8 lb), respectively. Sixty stifle joints were measured in group-2 dogs; mean TPA was 18.10 degrees , and mean age and weight of these dogs were 4.83 years and 35.85 kg (79 lb), respectively. The most common breeds included Labrador Retriever, Golden Retriever, and Rottweiler. The TPA of dogs in group 1 and group 1a were significantly greater than the TPA of dogs in group 2. CONCLUSIONS AND CLINICAL RELEVANCE: Dogs with CrCL injuries have a significantly greater TPA than dogs without CrCL injury. With further investigation, a normal TPA can be determined. In the future, TPA measurements may be used to screen dogs suspected of being susceptible to CrCL injury.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".