Incidence and risk factors for revision and contralateral anterior cruciate ligament reconstruction: A population-based retrospective cohort study
Bibliographic record
Abstract
There is a limited data on epidemiology of primary and recurrent anterior cruciate ligament reconstruction (ACLR) in Canada. The objectives of this study were to examine the incidence and factors associated with recurrent ACLR (revision and contralateral ACLR) in a western Canadian province of Alberta. We conducted a retrospective cohort study with an average follow up of 5.7 years. Albertans aged 10 to 60 years with a history of primary ACLR between 2010/11 to 2015/16 were included in the study. Participants were followed up until March 2019 to observe outcomes: Ipsilateral revision ACLR and contralateral ACLR. Kaplan Meir approach was used to estimate event free survival and Cox proportional hazard regression analysis was conducted to identify associated factors. Of the total participants with a history of primary ACLR on a single knee (n = 9292), n = 359, 3.9% (95% confidence interval: 3.5-4.3) underwent a revision ACLR. A similar proportion among those having primary ACLR on either knee (n = 9676), n = 344, 3.6% (95% confidence interval: 3.2-3.9) underwent a contralateral primary ACLR. Young age (<30 years) was associated with increased risk of contralateral ACLR. Similarly, young age (<30 years), having initial primary ACLR in winter and having allograft were associated with a risk of revision ACLR. Clinicians can use these findings in their clinical practice and designing rehabilitation plans as well as to educate patients about their risk for recurrent anterior cruciate ligament tear and graft failure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".