Assessment of methods of evaluating sarcopenia in old dogs
Bibliographic record
Abstract
OBJECTIVE: To evaluate clinically applicable methods of assessing lean body mass in dogs and compare muscle mass and inflammatory markers in healthy young and old dogs. ANIMALS: 9 healthy young (1 to 5 years old) and 10 old (> 8 years old) Labrador Retrievers with a body condition score of 5 to 6 of 9. PROCEDURES: Radiography of the thoracolumbar region was performed for measurement of epaxial muscle height at the level of T13-L1. Computed tomographic images were obtained for the measurement of the epaxial and temporal muscles. Ultrasonography also was performed for regional muscle measurements at these same sites and the quadriceps muscle. Serum C-reactive protein, insulin-like growth factor-1, and tumor necrosis factor-α concentrations also were measured, and dogs' activity for 14 days was assessed with an activity monitor. RESULTS: Mean epaxial muscle area measured by ultrasonography was significantly lower in the old group, compared with the young group, whereas epaxial muscle area measured by CT was only significantly lower in the old group after normalization for vertebral height. Neither temporal and quadriceps muscle measurements nor serum C-reactive protein or insulin-like growth factor-1 concentrations were significantly different between age groups. Tumor necrosis factor-α concentrations were undetectable in all dogs. CONCLUSIONS AND CLINICAL RELEVANCE: This study documented reduced epaxial muscle area in healthy old Labrador Retrievers, consistent with the syndrome of sarcopenia. Ultrasonography and CT were feasible methods of measuring epaxial muscle area, but much additional research is required to assess this method. A better understanding of underlying mechanisms of sarcopenia as well as methods for slowing progression is needed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".