Return-to-Play after ACL Reconstruction: A Single-Case Study of Multi-Context Biomechanical Monitoring in Collegiate Basketball
Bibliographic record
Abstract
Abstract Introduction Anterior cruciate ligament (ACL) injuries represent one of the most common and disruptive conditions in sport, with fewer than two-thirds of athletes return to competitive play. Effective return-to-play (RTP) monitoring requires multidimensional approaches that capture physical, psychological, and sport-specific components rather than reliance of isolated benchmarks. Purpose This study aimed to longitudinally examine the RTP process of a female varsity basketball athlete following ACL reconstruction, using a framework that integrates physical performance (capacity), biomechanical sport-specific (capability), and psychological (confidence) components relative to pre-injury benchmarks. Methods Data collection included countermovement jump testing with dual force plates, on-court inertial measurement unit (IMU) monitoring of limb-loading, and psychological questionnaires, analyzed relative to pre-injury and post-surgery baselines using minimal detectable change thresholds. Results Pre-injury monitoring indicated stable movement profiles with only minor fluctuations. Following ACL reconstruction, jump height recovered within seven weeks of RTP initiation, but notable inter-limb asymmetries persisted in force plate and IMU measures despite high confidence scores. Discussion Symmetry improved with continued training, yet variability in on-court loading remained even after clinical clearance. These findings highlight the value of integrated, multidimensional monitoring to detect residual deficits that may be overlooked by traditional outcome-based assessments. Conclusion This study demonstrates that integrating athlete-specific biomechanical, psychological, and sport-specific assessments relative to pre-injury baselines can support RTP decision-making to enhance individualized recovery trajectories in female athletes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".