Evaluating a Computer Vision-Based Rehabilitation Program for Osteosarcoma Patients: Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Abstract Background: Osteosarcoma can affect the function of the lower limbs when found in the distal femur and proximal tibia. Following total knee arthroplasty, patient may exhibit instability, limited range of motion, difficulty walking, reduced ability to bear weight, joint pain, joint dysfunction, and a decrease in daily life performance. This study aims to evaluate the efficacy of a computer vision-based intelligent rehabilitation program to manage patients post-operatively. Method: 96 patients with osteosarcoma requiring knee arthroplasty were recruited. Patients were randomized into either the intelligent rehabilitation group (IRG) or natural rehabilitation group (NRG) with an equal distribution. The main endpoints included the range of motion (ROM) of the knee joint and Knee injury and Osteoarthritis Outcome Score (KOOS). Secondary outcomes include 6-minute walk test (6MWT), Timed Up and Go test (TUG), the Toronto Extremity Salvage Score (TESS), SF-36 Scale Role Limitations due to Emotional Problems (RE), and Mental Health (MH) at three months and six months post-operatively. Results: In a 6-month study periodl, participants successfully concluded the trial.The IRG showed significant improvement in ROM (p=0.015), KOOS, 6-minute walk test (6MWT) (p=0.037), Timed Up and Go (TUG) test (p=0.041), Treatment Effectiveness and Satisfaction Survey (TESS) score (p=0.039), SF-36 Role Limitations due to Emotional Problems (RE) score (p<0.001) , and SF-36 Mental Health (MH) score (p<0.001) . Conclusion: This study provided evidence that the participants in the IRG showed considerable enhancements in joint function relative to the NRG. These findings confirmed the effectiveness of the computer vision-based intelligent rehabilitation in the post-surgery recovery of knee arthroplasty in osteosarcoma.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".