Can a Knee Brace Prevent Acl Re-Injury: A Systematic Review
Bibliographic record
Abstract
Background: Recent literature shows a rate of ACL retear after ACLR when returning to sport between 8% and 23%, depending on the population and clinical implications. The risk of a second injury is higher in patients who (1) return to cutting and pivoting sports, (2) do not meet the return to sport criteria before returning to sport, and (3) returning to pivoting sports earlier than 9 months after ACLR. A second ACL injury, either a graft rupture or contralateral ACL injury after ACLR, negatively impacts knee function, quality of life, accelerates degenerative changes in the knee and challenges an athlete’s career. Purpose: This systematic review aimed to investigate whether a knee brace when returning to sport (RTS) could prevent a second injury after anterior cruciate ligament reconstruction (ACLR). Methods: This study was registered with the PROSPERO database and followed PRISMA guidelines. A systematic search of PubMed, Ovid Medline, Ovid All EBM Reviews, Ovid Embase, EBSCO Sportdiscus and ISI Web of Science databases for meta-analysis, randomized controlled trials and prospective cohort studies published before July 2020 was undertaken. The inclusion criteria were: (1) Comparing with and without brace at RTS, (2) follow up of at least 18 months after ACLR, (3) reinjury rates included in the outcomes. Data were extracted independently by two reviewers. Quality appraisal analyses were performed for each study using the Cochrane Collaboration tools for randomized and nonrandomized trials. Results: A total of 1196 patients in 3 studies were included. One study showed a lower rate of reinjury when wearing a knee brace at RTS. One study found the knee brace to have a significant protective effect for younger patient. (p < 0.05). Conclusion: Current data cannot support that using a knee brace when RTS will decrease the rate of reinjury after ACL reconstruction. [Table: see text][Figure: see text][Table: see text][Table: see text][Table: see text][Table: see text]
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".