Intraoperative and Postoperative Outcomes of Patients Undergoing Total Knee Arthroplasty With Prior Anterior Cruciate Ligament Reconstruction: A Matched Cohort Analysis
Bibliographic record
Abstract
Background Previous anterior cruciate ligament (ACL) injury is a risk factor for the development of knee osteoarthritis. Despite advances in ACL reconstruction (ACLR) techniques, many patients with history of ACLR develop end-stage osteoarthritis necessitating total knee arthroplasty (TKA). The purpose of this study was to investigate the impact of prior ACLR on intraoperative and postoperative outcomes of TKA. Methods This was a single-centre matched cohort study of all patients with prior ACLR undergoing primary TKA from January 2000 to May 2022. Patients were matched 1:1 to patients undergoing TKA with no prior ACL injury based on age, sex, and body mass index. Outcomes investigated included TKA procedure duration, soft-tissue releases, implant design, and complications requiring reoperation. Results Forty-two ACLR patients were identified and matched to controls. Mean follow-up was 6.8 years and 5.0 years in the ACLR and control cohorts, respectively ( P = .115). ACLR patients demonstrated longer procedure durations (122.8 minutes vs 87.0 minutes, P < .001) and more frequently required soft-tissue releases (40.5% vs 14.3%, P = .007), stemmed implants (23.8% vs 4.8%, P = .013), and patellar resurfacing (59.5% vs 26.2%, P = .002). There were no significant differences in postoperative clinical or surgical outcomes between groups. Ten-year implant survivorship was 92% and 95% in the ACLR and control cohorts, respectively ( P = .777). Conclusions TKA is an effective procedure for the management of end-stage osteoarthritis with prior ACLR. The care team should be prepared for longer operative times and the utilization of advanced techniques to achieve satisfactory soft-tissue balance and implant stability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".