Surgical Techniques, Outcomes, Indications, and Complications of Simultaneous High Tibial Osteotomy and Anterior Cruciate Ligament Revision Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The incidence of primary anterior cruciate ligament reconstruction (ACLR) failure ranges from 10 to 20% in the USA. Many patient and surgical factors may lead to re-rupture after ACLR. Some authors have suggested that not correcting excessive posterior tibial slope may be a significant contributing factor to ACLR failure. PURPOSES: We sought to systematically review the literature on outcomes, indications, and complications in patients undergoing simultaneous high tibial osteotomy (HTO) and ACLR revision. METHODS: PubMed, Medline, and Embase were searched in February 2018 for articles addressing simultaneous HTO and ACLR revision in skeletally mature patients. Major orthopedic conferences were screened in duplicate to find gray literature. All studies were assessed using the Methodological Index for Non-Randomized Studies. Descriptive statistics are presented where applicable. RESULTS: 13). Mean delay between primary and revision surgery was 9 years. Rehabilitation protocol dictated return to running at 4 months and return to sport at 4 to 9 months. Visual analog scale pain scores reduced on average by 30 points. Subjective International Knee Documentation Committee, Tegner-Lysholm, and Tegner activity scores also improved. Fifty-eight percent (35/60) of patients showed osteoarthritis signs post-operatively. One patient (1.3%) received an arthroscopic arthrolysis of adhesions for stiffness. There were no reported incidences of graft re-rupture. CONCLUSION: This systematic review found that the use of HTO for ACLR revision produces good post-operative functional outcomes, low complication rates, and no reported re-ruptures. The main indications for combined HTO with ACLR revision was a posterior slope of more than 12° or severe varus malalignment. Future studies with large sample sizes and long-term follow-up are required to corroborate these results.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".