ArticleDigital health
Machine learning models for predicting return to sports after anterior cruciate ligament reconstruction: Physical performance in early rehabilitation.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Return to Sport After Anterior Cruciate Ligament Injury: A Scopus-Based Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2026Review
- Deep Learning for Sensor-Based Sport Performance and Health Monitoring: A Review of Wearable, Vision-Based, and Multimodal Sensing Approaches.Sensors (Basel, Switzerland) · 2026Review
- Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.BMC medical informatics and decision making · 2026Article
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Application of artificial intelligence in postoperative orthopedic rehabilitation: a scoping review.Frontiers in digital health · 2025Review
- From injury to comeback: A systematic review of machine learning models predicting return to sport in athletes.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Return to sports (RTS) after anterior cruciate ligament reconstruction (ACLR) is a crucial surgical success measure. In this study, we aimed to identify the best-performing machine learning models for predicting RTS at 12 months post-ACLR, based on physical performance variables at 3 months post-ACLR. Methods: This case-control study included 102 patients who had undergone ACLR. The physical performance variables measured 3 months post-ACLR included the Biodex balance system, Y-balance test, and isokinetic muscle strength test. The RTS outcomes measured at 12 months post-ACLR included the single-leg hop test, single-leg vertical jump test, and Tegner activity score. Six machine learning algorithms were trained and validated using these data. Results: Random forest models in the test set best predicted the RTS success based on the single-leg hop test (area under the curve [AUC], 0.952) and Tegner activity score (AUC, 0.949). Gradient boosting models in the test set best predicted the RTS based on the single-leg vertical jump test (AUC, 0.868). Conclusion: Modifiable factors should be considered in the early rehabilitation stage after ACLR to enhance the possibility of a successful RTS.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.