Meniscus repair with simultaneous ACL reconstruction demonstrated similar clinical outcomes as isolated ACL repair: a result not seen with meniscus resection
Bibliographic record
Abstract
PURPOSE: To compare Knee Injury and Osteoarthritis Outcome Score (KOOS) and EuroQol-5D (EQ-5D) subscale scores at 2-year follow-up for patients with primary isolated ACL reconstruction with patients undergoing ACL reconstruction and simultaneous meniscal treatment in terms of either resection or repair in the Swedish National Knee Ligament Register (SNKLR). METHODS: All ACL reconstruction patients within the SNKLR at 2-year follow-up were reviewed. The KOOS and EQ-5D subscales were assessed in four distinct patient groups: isolated ACL reconstruction, ACL reconstruction + medial meniscus resection, ACL reconstruction + lateral meniscus resection, ACL reconstruction + medial meniscus repair, and ACL reconstruction + lateral meniscus repair. The primary analysis was conducted using linear regression with isolated ACL reconstruction designated as the reference group, and was adjusted for patient age, gender, and time from injury to surgery. RESULTS: The included patients consisted of 10,001 (65.0%) individuals with an isolated ACL injury, 588 (3.8%) with ACL injury plus treated with medial meniscus repair, 2307 (15.0%) with ACL injury plus treated with medial meniscus resection, 323 (2.1%) with ACL injury plus treated with lateral meniscus repair, and 2173 (14.1%) with ACL injury plus treated with lateral meniscus resection. Meniscus resection demonstrated significantly worse results with respect to the KOOS Symptoms subscale for both the medial and lateral meniscus resection groups. Medial meniscus resection also demonstrated worse results for the KOOS quality of life (QoL) subscale, while lateral meniscus resection only approached significance. Outcomes were not different between the isolated ACL reconstruction group and the meniscus repair groups. CONCLUSION: Meniscus resection in addition to ACL reconstruction resulted in worse clinical outcomes than isolated ACL reconstruction patients; a result not seen within the meniscus repair group. This suggests that, when possible, meniscus repair may provide greater clinical outcomes over resection when treating a reparable meniscal tear that presents along with an ACL tear. Clinicians should consider and implement these findings for the management of future meniscus tear patients within their clinical practice. LEVEL OF EVIDENCE: Level III.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".