Comparison of computed tomographic attenuation values for epaxial muscles in old and young dogs
Bibliographic record
Abstract
OBJECTIVE To determine whether the degree of CT attenuation of muscle would differ between healthy old and young dogs. ANIMALS 10 healthy old (> 8 years old) and 9 healthy young (1 to 5 years old) Labrador Retrievers with a body condition score of 5 or 6 on a 9-point scale. PROCEDURES CT was performed with the dogs mildly sedated. A freehand closed polygon tool was used to define the outer margin of the left epaxial muscles on each transverse image obtained from the cranial to caudal endplates of T13. The CT attenuation values from every voxel from within these regions of interest were exported from DICOM software as a single dataset in an extensible markup language file. From these data, mean CT attenuation values were calculated for each dog and these mean values were compared between age groups. RESULTS Mean CT attenuation values for the epaxial muscles were significantly lower in old dogs than in young dogs. A significant negative correlation (r = -0.74) was identified between mean CT attenuation values and dog age. CONCLUSIONS AND CLINICAL RELEVANCE In addition to loss of skeletal muscle mass, low muscle CT attenuation values suggested that the old dogs in this study also had greater muscle fat content than did young dogs. Additional studies are warranted to evaluate qualitative and quantitative muscle changes in old dogs.
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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".