Lateral Extra‐articular Tenodesis Does Not Decrease Graft Failure in Revision Anterior Cruciate Ligament Reconstruction When Combined With Quadriceps or Patellar Tendon Grafts
Bibliographic record
Abstract
PURPOSE: To compare return-to-sport (RTS) rates, graft failure rates, and clinical outcomes in patients who underwent revision anterior cruciate ligament reconstruction (R-ACLR) with additional lateral extra-articular tenodesis (LET) versus isolated R-ACLR. METHODS: A retrospective review of the medical records of patients who underwent R-ACLR with or without a modified Lemaire LET procedure was performed. Seventy-four patients with at least 2 years of follow-up who had high-grade positive pivot-shift test findings were included. Concomitant procedures such as meniscectomy and meniscal repair were collected, along with any complications and/or graft failure. The Knee Injury and Osteoarthritis Outcome Score (KOOS) and the International Knee Documentation Committee Subjective Knee Form score were collected. The ability to RTS was defined as fully, partially, or not returned. RESULTS: Of the patients, 39 underwent isolated R-ACLR (mean age ± standard deviation, 29.2 ± 12.2 years) whereas 35 underwent an additional LET procedure (mean age, 24.6 ± 7.4 years). The mean length of follow-up in the R-ACLR group was 56.6 ± 26.5 months compared with 44.3 ± 17.6 months in the R-ACLR-LET group (P = .02) (range, 24-120 months). Patient-reported outcome measures were higher in the R-ACLR-LET group, with the KOOS Activities of Daily Living (93.5 ± 2.0 vs 97.2 ± 1.6, P = .03) and KOOS Sport (63.0 ± 3.6 vs 74.3 ± 3.8, P = .05) subdomain scores reaching the level of statistical significance. No differences were found in the other KOOS subdomain scores or the International Knee Documentation Committee scores. Failure rates were not significantly different between the groups (12.8% for R-ACLR vs 11.4% for R-ACLR-LET, P = .99). There were 13 patients (72.2%) in the R-ACLR group and 14 patients (60.8%) in the R-ACLR-LET group who did not RTS. CONCLUSIONS: R-ACLR with additional LET showed similar failure and RTS rates to isolated R-ACLR after failed ACLR. The R-ACLR-LET group showed better functional results with significantly higher KOOS subdomain scores for activities of daily living, as well as sports and recreation. However, this study was unable to recommend the modified Lemaire LET procedure to be routinely used in R-ACLR patients. LEVEL OF EVIDENCE: Level III, retrospective comparative therapeutic trial.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".