A comparative study of traditional vs AI-assisted rehabilitation methods for lower limb injuries in basketball players: a 12-month semi-experimental follow-up
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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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".