A comparative study of traditional vs AI-assisted rehabilitation methods for lower limb injuries in basketball players: a 12-month semi-experimental follow-up
Why this work is in the frame
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Bibliographic record
Abstract
Introduction: Injury prevention and rehabilitation are fundamental components of modern sports science and athlete management. Sports injuries not only negatively impact athletic performance and career longevity. Method: This study aims to compare the effectiveness of traditional rehabilitation protocols versus modern rehabilitation programs assisted by Artificial Intelligence (AI) techniques in basketball players who sustained lower-limb injuries (knee, ankle, or musculature). Result: Primary outcomes include time to return to play (RTP), changes in physical performance indicators (muscular strength, explosive power, balance, shooting accuracy), and re-injury rates over a 12-month follow-up period. Secondary outcomes examine athlete and clinician satisfaction with each protocol. Conclusion: The trial uses baseline measurement, 3-month and 6-month post-intervention assessments, and a 12-month tracking of re-injury. It is hypothesized that the AI-assisted group will demonstrate shorter RTP time, superior gains in performance measures, and lower re-injury incidence compared to the traditional group. These findings may inform rehabilitation practice in sport and support evidence-based adoption of AI tools in athlete recovery programs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it