Validation of two-dimensional kinematic analysis of walk and sit-to-stand motions in dogs
Bibliographic record
Abstract
OBJECTIVE: To assess the intra- and interobserver repeatability of 2-dimensional (2-D) kinematic analysis of walk and sit-to-stand motions in dogs. ANIMALS: 10 healthy adult Labrador Retrievers. PROCEDURES: 10 dogs were filmed during walk and sit-to-stand motions. Five trials were recorded for each dog, 3 of which were digitized. Two observers manually marked 15 landmarks on each frame during the motions of interest for these 3 trials. Each observer repeated the procedure approximately 1 week later. The 2-D joint angles were calculated. Intra- and interobserver coefficients of multiple correlations (CMCs) were calculated for each joint angle-time history. RESULTS: Intraobserver repeatability, assessed as the mean CMCs of 12 joint angle measurements made for 10 dogs by 2 observers, was good or excellent in 23 of 24 (96%) mean CMCs of the joints measured. Interobserver variation, assessed by comparing CMCs of measurements made by 2 observers on 10 dogs on 2 days, was good or excellent in 161 of 240 (67%) CMCs of joints measured. CONCLUSIONS AND CLINICAL RELEVANCE: Intraobserver repeatability of 2-D kinematic measurements made on digitized videotapes was excellent. Interobserver repeatability of these measurements was acceptable.
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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 | 0.004 | 0.014 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".