MétaCan
Menu
← Back to cohort

Effectiveness of Proprioceptive and Plyometric Training on Joint Stability in Athletes Following ACL Injury v1

2025· article· en· W4413317692 on OpenAlexaboutno aff
Zahid Hossain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPlyometricsAthletesProprioceptionPhysical medicine and rehabilitationJoint stabilityPhysical therapyJoint (building)Training (meteorology)MedicineEngineeringStructural engineeringJumpGeographyPhysics

Abstract

fetched live from OpenAlex

Background: Anterior cruciate ligament (ACL) injury is among the most common sports-related injuries and is associated with reduced joint stability, impaired functional performance, and psychological barriers to returning to sport. Although conventional physiotherapy focusing on strengthening and mobility is widely used, recent evidence suggests that proprioceptive and plyometric training may provide additional benefits by enhancing neuromuscular control, balance, and confidence. However, limited randomized controlled trials have comprehensively examined their combined effectiveness on joint stability and return-to-sport readiness in athletes following ACL injury.Objective: The purpose of this randomized controlled trial is to evaluate the effectiveness of proprioceptive and plyometric training, when combined with conventional physiotherapy, in improving joint stability, reducing pain, enhancing functional outcomes, and promoting psychological readiness for return to sport in athletes following ACL injury.Methods: A total of 62 athletes with unilateral ACL reconstruction will be recruited and randomly assigned into two groups: (i) intervention group (n = 31), receiving proprioceptive and plyometric training in addition to conventional physiotherapy, and (ii) control group (n = 31), receiving conventional physiotherapy alone. The intervention will be carried out over 8 weeks, with supervised sessions two days per week. The primary outcome will be joint stability assessed by the Star Excursion Balance Test (SEBT). Secondary outcomes will include pain measured by the Visual Analog Scale (VAS), postural control assessed by the Berg Balance Scale (BBS), knee function evaluated using the International Knee Documentation Committee (IKDC) subjective score, functional return to sport measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and psychological readiness assessed using the ACL–Return to Sport after Injury (ACL-RSI) scale. Outcomes will be measured at baseline and after the intervention.Expected Results: It is anticipated that athletes in the intervention group will demonstrate greater improvements in joint stability, pain reduction, balance, knee function, and psychological readiness compared to the control group. Proprioceptive and plyometric training are expected to enhance neuromuscular control and confidence, thereby facilitating safer and earlier return to sport.Conclusion: This study will provide valuable evidence on the role of proprioceptive and plyometric training in post-ACL rehabilitation. If effective, these approaches may be recommended as integral components of physiotherapy programs to optimize recovery, reduce re-injury risk, and improve long-term athletic performance.Keywords: ACL injury, proprioceptive training, plyometric training, joint stability, rehabilitation, SEBT, IKDC, WOMAC, ACL-RSI.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.021
GPT teacher head0.301
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicKnee injuries and reconstruction techniques→French-language works237,207→