Coxofemoral joint kinematics using video fluoroscopic images of treadmill-walking cats: development of a technique to assess osteoarthritis-associated disability
Bibliographic record
Abstract
The objectives of this pilot study were to develop a video fluoroscopy kinematics method for the assessment of the coxofemoral joint in cats with and without osteoarthritis (OA)-associated disability. Two non-OA cats and four cats affected by coxofemoral OA were evaluated by video fluoroscopy. Video fluoroscopic images of the coxofemoral joints were captured at 120 frames/s using a customized C-arm X-ray system while cats walked freely on a treadmill at 0.4 m/s. The angle patterns over time of the coxofemoral joints were extracted using a graphic user interface following four steps: (i) correction for image distortion; (ii) image denoising and contrast enhancement; (iii) frame-to-frame anatomical marker identification; and (iv) statistical gait analysis. Reliability analysis was performed. The cats with OA presented greater intra-subject stride and gait cycle variability. Three cats with OA presented a left-right asymmetry in the range of movement of the coxofemoral joint angle in the sagittal plane (two with no overlap of the 95% confidence interval, and one with only a slight overlap) consistent with their painful OA joint, and a longer gait cycle duration. Reliability analysis revealed an absolute variation in the coxofemoral joint angle of 2º-6º, indicating that the two-dimensional video fluoroscopy technique provided reliable data. Improvement of this method is recommended: variability would likely be reduced if a larger field of view could be recorded, allowing the identification and tracking of each femoral axis, rather than the trochanter landmarks. The range of movement of the coxofemoral joint has the potential to be an objective marker of OA-associated disability.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".