Risk Factors for Recurrent Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
Background: Anterior cruciate ligament reconstruction (ACLR) is routinely performed for symptomatic instability. Although it is a common procedure, there remain differences in surgical technique. Hospital administrative records in a public health care system were used to investigate the effect of patient, provider, and surgical factors on the risk of revision ACLR. Purpose: To define the rate and risk factors for ACL reoperation in Ontario, Canada, including both ipsilateral revision and contralateral primary procedures. Study Design: Cohort study; Level of evidence, 3. Methods: All primary elective ACLR procedures performed in Ontario (July 2003 to March 2008) in patients aged 15 to 60 years were identified via physician billing and hospital databases. Revision and contralateral ACLR were sought until January 2012. Patient factors (age, sex, comorbidity, income quintile, length of index hospital admission), provider factors (surgeon volume, academic hospital status), and surgical factors (allograft vs autograft; fixation type [screw, button, staple]; concomitant operative procedures) were used as covariates in a Cox proportional hazards survivorship model to generate hazard ratios (HRs) with confidence intervals (CIs) (α = .05). Kaplan-Meier survivorship curves with ACL revision as the end point were generated. Results: A total of 12,967 ACLR procedures with a mean follow-up of 5.2 years were eligible for study using preset criteria. The revision rate was 2.6% (mean ± SD, 2.91 ± 1.71 years to revision). The rate of primary contralateral ACLR was 4.6% (mean, 2.95 ± 1.81 years). In the Cox model, younger age (15-19 years) (HR, 2.1; 95% CI, 1.5-2.9; P < .001), ACLR performed at an academic hospital (HR, 1.6; 95% CI, 1.2-2.1; P < .001), and the use of allograft (HR, 1.7; 95% CI, 1.1-2.6; P = .02) significantly increased the risk of revision ACLR. Only younger age (HR, 2.1; 95% CI, 1.6-2.7; P < .001) was associated with an increased risk of contralateral ACLR. Conclusion: Contralateral ACLR was more frequent than revision ACLR in this population, while both surgical procedures were most common in patients younger than 20 years. Academic hospital status, but not surgeon volume, as well as the use of allograft also increased the risk for revision ACLR.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".