The Effects of Different Management Strategies or Rehabilitation Approaches on Knee Joint Structural and Molecular Biomarkers Following Traumatic Knee Injury: A Systematic Review of Randomized Controlled Trials for the OPTIKNEE Consensus
Bibliographic record
Abstract
OBJECTIVE: To summarize the effectiveness of management strategies and rehabilitation approaches for knee joint structural and molecular biomarker outcomes following anterior cruciate ligament (ACL) and/or meniscal tear. DESIGN: Intervention systematic review. LITERATURE SEARCH: We searched the MEDLINE, Embase, CINAHL, CENTRAL, and SPORTDiscus databases from their inception up to November 3, 2021. STUDY SELECTION CRITERIA: We included randomized controlled trials (RCTs) investigating the effectiveness of management strategies or rehabilitation approaches for structural/molecular biomarkers of knee joint health following ACL and/or meniscal tear. DATA SYNTHESIS: We included 5 RCTs (9 papers) with primary ACL tear (n = 365). Two RCTs compared initial management strategies (rehabilitation plus early vs optional delayed ACL surgery), reporting on structural biomarkers (radiographic osteoarthritis, cartilage thickness, meniscal damage) in 5 papers and molecular biomarkers (inflammation, cartilage turnover) in 1 paper. Three RCTs compared different post-ACL reconstruction (ACLR) rehabilitation approaches (high vs low intensity plyometric exercises, accelerated vs nonaccelerated rehabilitation, continuous passive vs active motion), reporting on structural biomarkers (joint space narrowing) in 1 paper and molecular biomarkers (inflammation, cartilage turnover) in 2 papers. RESULTS: There were no differences in structural or molecular biomarkers between post-ACLR rehabilitation approaches. One RCT comparing initial management strategies demonstrated that rehabilitation plus early ACLR was associated with greater patellofemoral cartilage thinning, elevated inflammatory cytokine response, and reduced incidence of medial meniscal damage over 5 years compared to rehabilitation with no/delayed ACLR. CONCLUSION: Very low-certainty evidence suggests that different initial management strategies (rehabilitation plus early vs optional delayed ACL surgery) but not postoperative rehabilitation approaches may influence the incidence of meniscal damage, patellofemoral cartilage loss and cytokine concentrations over 5 years post-ACL tear. J Orthop Sports Phys Ther 2023;53(4):172–193. Epub: 20 February 2023. doi:10.2519/jospt.2023.11576
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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 | 0.028 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".