Noninvasive determination of body segment parameters of the hind limb in Labrador Retrievers with and without cranial cruciate ligament disease
Bibliographic record
Abstract
OBJECTIVE: To determine mass, center of mass (COM), and moment of inertia (ie, body segment parameters [BSPs]) of hind limb segments by use of a noninvasive method based on computerized tomography (CT) in Labrador Retrievers with and without cranial cruciate ligament (CCL) disease and to provide regression equations to estimate BSPs of normal, CCL-deficient, and contralateral hind limbs. ANIMALS: 14 clinically normal and 10 CCL-deficient Labrador Retrievers. PROCEDURES: Bone, muscle, and fat areas were identified via CT. Mass, COM, and moment of inertia were determined on the basis of tissue densities in the thigh, crus, and foot segments. Regression models were developed to determine predictive equations to estimate BSP on the basis of simple morphometric measurements. RESULTS: The thigh and crus of CCL-deficient limbs weighed less than in contralateral segments. Thighs weighed less in CCL-deficient than in normal limbs. The thigh moment of inertia was less in CCL-deficient than in contralateral limbs. The crural COM was located more distally in normal limbs, compared with other limbs. Predictive equations to estimate BSP varied by parameter, body segment, and limb status. CONCLUSIONS AND CLINICAL RELEVANCE: BSPs of the thigh and crus varied with segment and status of the hind limb in Labrador Retrievers with or without CCL disease. Equations to estimate BSP on the basis of simple morphometric measurements were proposed, providing a basis for nonterminal studies of inverse dynamics of the hind limbs in Labrador Retrievers. This approach may offer new strategies to investigate the pathogenesis of nontraumatic joint diseases.
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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 | 0.001 | 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) | 0.001 | 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".