244 Gait changes in the aging canine.
Bibliographic record
Abstract
Abstract With aging comes changes in gait and mobility in our companion animals. While aspects of these mobility changes have been quantified in humans, less have been identified in dogs. The aim of this retrospective data analysis was to identify age-related gait changes in Labrador Retrievers. Data collected in March of 2018 was available on 65 dogs, of which 34 had data collected again in August of 2024 and were at that time without known mobility issues based on kennel records. Of those 34 dogs, 16 (8 males, 8 females) had reached at least 10 years of age and were used in the final data analysis. Average age at the first data collection was 5.5 years old, and after the 6 year span average age increased to 11.9 years and body weights at this time averaged 31.8 ± 3.18 kg. Gait was analyzed using the Gait4Dogs system which measures spatial, temporal, and pressure variables for each limb. For each variable, the sum of the front limb or left limb values were divided by the sum of the hind limb or right limb values to generate the front:hind (H:R) or left:right (L:R) ratios, respectively. The average value for each gait variable was also calculated. Data were analyzed in SAS using a repeated measures mixed model with fixed effects of time. Age in 2018 was included as a covariate and type 1 sum of squares was performed. Sex was included as a random effect and dog as the repeated subject. We observed significant decreases in cadence, average step length, average stride length, and average swing percentage of cycle (P ≤ 0.05). Conversely, average step time, cycle time, swing time, stance percentage of cycle, and stance time all increased as dogs aged (P ≤ 0.05). Front:hind ratios for step time, cycle time, swing percentage of cycle, and swing time all decreased over time (P ≤ 0.01), while stance percentage of cycle and stance time F:H ratios increased (P ≤ 0.01). Overall, we observed that as Labrador Retrievers aged, they developed slower gaits with reduced step and stride lengths and a shift towards spending more time in the stance phase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".