Does a Delay in Anterior Cruciate Ligament Reconstruction Increase the Incidence of Secondary Pathology in the Knee? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Previous systematic reviews looking at timing of anterior cruciate ligament reconstruction (ACLR) examined the functional outcomes and range of motion; however, few have quantified the effect of timing of surgery on secondary pathology. The goal of this study was to analyze the effects of early ACLRs versus delayed ACLR on the incidence of meniscal and chondral lesions. DATA SOURCES: We searched MEDLINE, EMBASE, and CINAHL on March 20, 2018, for randomized control trials (RCTs) that compared early and delayed ACLR in a skeletally mature population. Two reviewers independently identified trials, extracted trial-level data, performed risk-of-bias assessments using the Cochrane Risk of Bias tool, and evaluated the study methodology using the Detsky scale. A meta-analysis was performed using a random-effects model with the primary outcome being the total number of meniscal and chondral lesions per group. RESULTS: Of 1887 citations identified from electronic and hand searches, we included 4 unique RCTs (303 patients). We considered early reconstruction as <3 weeks and delayed reconstruction as >4 weeks after injury. There was no evidence of a difference between early and late ACLR regarding the incidence of meniscal [relative risk (RR), 0.98; 95% confidence interval (CI), 0.74-1.29] or chondral lesions (RR, 0.88; 95% CI, 0.59-1.29), postoperative infection, graft rupture, functional outcomes, or range of motion. CONCLUSIONS: We found no evidence of benefit of early ACLR. Further studies may consider delaying surgery even further (eg, >3 months) to determine whether there are any real benefits to earlier reconstruction.
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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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.045 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".