Variation in the Quantity of Elastic Fibres with Degeneration in Canine Cranial Cruciate Ligaments from Labrador Retrievers
Bibliographic record
Abstract
Abstract Objectives This study aims to quantify numbers of elastic fibres in cranial cruciate ligaments from a dog breed at high risk of cranial cruciate ligament disease. Methods Macroscopically normal cranial cruciate ligaments were harvested from six Labrador retrievers. Sequential histological sections were assessed for extracellular matrix degeneration (haematoxylin and eosin stain) and elastic fibre staining (Miller’s stain). Elastic fibres were semi-quantified using previously published scoring systems. Each section was scored twice by two observers. Results Increased numbers of elastic fibres were seen with increasing cranial cruciate ligament degeneration (p = 0.001). Labrador retriever cranial cruciate ligaments had lower elastic fibre staining when compared with previous published findings in the racing greyhound. Clinical Significance The cranial cruciate ligaments from a dog breed at high risk of cranial cruciate ligament disease vary in the quantity of elastic fibres in association with ligament degeneration. Breed variation in the quantity of elastic fibres may reflect differing risk of cranial cruciate ligament disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".