The Epidemiology of Revision Anterior Cruciate Ligament Reconstruction in Adults from Ontario, Canada
Bibliographic record
Abstract
Objectives: The morbidity associated with revision anterior cruciate ligament reconstruction (ACLR) is largely unknown. The objective of this study was to determine the rate of and risk factors for re-revision, re-operation, and re-admission following revision ACLR in the general population. Methods: All patients who underwent first revision ACLR in Ontario, Canada from January 2004 to December 2010 were identified and followed to December 2012. Exclusions included age (<16 years), previous osteotomy, or multi-ligament knee reconstruction. The main outcome was re-revision ACLR. Secondary outcomes included re-operation [irrigation and debridement (I&D), meniscectomy, manipulation under anesthesia (MUA), contralateral ACLR, and total knee arthroplasty (TKA)], and re-admission within 90 days of surgery. Survival to re-revision was determined using the Kaplan-Meier (KM) approach. A Cox proportional hazards model or logistic regression were used to determine the influence of patient factors (age, sex, neighborhood income quintile, and comorbidity), surgical factors (graft choice, concurrent meniscal procedure, and fixation method), and provider factors (surgeon volume, surgeon years in practice, and hospital status) on outcomes. A post-hoc analysis was performed to determine the influence of the aforementioned factors on overall post-operative infection risk, including both operative and non-operative cases. Results: Overall, 827 patients were included (median age: 30 years; 58.8% males). Single stage revisions comprised 92.9% of cases, and a meniscal procedure (repair or debridement) was performed in 45.3% of cases. The re-revision rate at a mean follow-up of 4.8±2.2 years was 4.4%, and the five-year survival rate was 95.4% (Figure 1). The rates of I&D, meniscectomy, contralateral ACLR, and re-admission were 0.8%, 3.1%, 3.4%, and 4.1%, respectively. MUA and TKA were rare. Young age significantly increased contralateral ACLR risk (risk decreased by 5.1% with each year of age above 16 years, p=0.02), but not re-revision ACLR risk. Low surgeon annual volume of revision ACLR [<4 revisions/year: odds ratio (OR) 1.2, p=0.02)] and male sex (OR 13.3, p=0.01) significantly increased overall infection risk, while male sex also influenced I&D risk. No other factors significantly influenced re-revision, re-operation, or re-admission risk. Conclusion: Re-revision, re-operation, and re-admission rates following revision ACLR are low. The risk of I&D, overall infection, and contralateral ACLR were influenced by male sex, low surgeon volume, and young age, respectively. This is the first study of this magnitude to determine rates of and risk factors for morbidity following revision ACLR, providing clinicians with reference data from the general population.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".