The Risk of Knee Arthroplasty Following Cruciate Ligament Reconstruction A Population-Based Matched Cohort Study
Bibliographic record
Abstract
Background: Evidence regarding the risk of end-stage osteoarthritis following cruciate ligament reconstruction is based upon small sample sizes and radiographic, rather than clinical, criteria. The goals of this study were to determine the risk of knee arthroplasty, a surrogate for end-stage osteoarthritis, following cruciate ligament reconstruction, and to identify patient, provider, and surgical factors that influence knee arthroplasty risk. Methods: Using administrative databases, we identified all patients who were sixteen to sixty years of age and had undergone cruciate ligament reconstruction inOntario from July 1993 toMarch2008. Casepatientswerematchedby demographic variables to five individuals without knee injury from the general population of Ontario, Canada, who had not undergone previous knee surgery, including cruciate ligament reconstruction. The main outcome was knee arthroplasty. Kaplan-Meier survival curves were generated for both cohorts. A Cox proportional hazards model determined those factors that influenced knee arthroplasty risk. Results: We identified 30,301 eligible patients who had undergone cruciate ligament reconstruction; of these patients, 30,277werematched to 151,362 individuals from the general population; themedian patient agewas twenty-eight years and 65 % of the patients were male. Primary anterior cruciate ligament reconstruction accounted for>98 % of index cases. During the follow-up period, there was a significant difference (p < 0.001) betweenmatched case and control cohorts with respect to the number of patients who underwent knee arthroplasty during the study period; in the matched case cohort, 209 patients underwent knee arthroplasty (event rate, 0.68 of 1000 person-years), and in the control cohort, 125 patients underwent knee
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".