Psychological readiness and return to sports after ACL reconstruction: A meta-analysis of KOOS scale outcomes at one- and two-year follow-up
Bibliographic record
Abstract
Background The Knee Injury and Osteoarthritis Outcome Score (KOOS) is widely used to evaluate functional outcomes after anterior cruciate ligament reconstruction (ACLR). However, there is limited consolidated evidence on subscale-specific recovery trends over time. Objective To assess the effectiveness of rehabilitation in improving KOOS subscale scores Pain, Symptoms, Activities of Daily Living (ADL), Quality of Life (QoL), and Sport/Recreation (Sport/Rec) at one- and two-year follow-up post-ACLR. Methods This PRISMA-compliant meta-analysis (PROSPERO: CRD42025646262) used PICOS criteria. Ten studies were selected from PubMed, Web of Science, and Scopus between 2019 and 2025. Methodological quality was assessed using the Newcastle–Ottawa Scale and GRADE. Random-effects models calculated standardized mean differences (SMDs); heterogeneity was assessed with I 2 and τ 2 . Results Ten studies (n = 6,407–6,434 per subscale) were analyzed. KOOS QoL showed the greatest improvement (SMD = 0.46; 95% CI: 0.093 to 0.835; p = 0.0197), followed by Pain (SMD = 0.37; 95% CI: –0.02 to 0.76; p = 0.063). Symptoms showed a small-to-moderate effect (SMD = 0.44; p = 0.074), while ADL presented limited improvement (SMD = 0.21; p = 0.1015). Sport/Rec scores improved modestly (SMD = 0.37; p = 0.0732), with large individual gains seen in athletes with lower baseline scores. Heterogeneity was substantial across subscales (I 2 = 84.3%–90.9%). Conclusion Rehabilitation after ACLR leads to meaningful improvements in KOOS Pain, QoL, and Symptoms scores. Sport/Rec and ADL also improved, especially in long-duration programs. KOOS is a valuable outcome tool and should be integrated with objective return-to-sport criteria for comprehensive assessment.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.059 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".