Association between radiographic anterior cruciate ligament tear and joint symptoms: Data from the osteoarthritis initiative
Bibliographic record
Abstract
INTRODUCTION: Symptomatic osteoarthritis (OA) in the knee is defined as the presence of OA radiographic features in combination with knee symptoms. Pain has not been shown to correlate meaningfully to radiographic severity. We aimed to determine the relationship between a tear of the anterior cruciate ligament (ACL) with knee symptoms and radiographic OA. METHODS: A within-person, between-knee cross-sectional study of 37 participants from the Osteoarthritis Initiative (OAI) with a complete or partial ACL tear detected on magnetic resonance imaging in 1 knee (index knee) were included. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee Injury and Osteoarthritis Outcome Score (KOOS) and radiographs of both knees, 1 with an ACL tear and one without (control knee) were scored for OA severity (Kellgren-Lawrence Grading) and symptoms. A generalized estimating equation with linear regression was used to compare symptom scores within individuals as well as to radiographic severity. RESULTS: ) reported no difference in knee symptoms (WOMAC pain odds ratio [OR] =1.92, 95%CI 0.699-5.248, P = .21; KOOS symptoms OR = 2.12, 95%CI 0.740-6.065, P = .09), stiffness (OR = 1.67, 95%CI 0.653-5.583, P = .35) or functional disability (OR = 1 0.97, 95%CI 0.515-7.508, P = .32) in the knee that exhibited an ACL tear compared to the control knee. Only knee function and disability (WOMAC Disability OR = 1.12, 95%CI 1.003-1.249, P = .04) were associated with radiographic severity between index and control knees. CONCLUSION: Individuals did not report an increase in knee pain, stiffness or disability in their ACL-deficient knee. Only disability was associated with worsening severity of radiographic OA in ACL-deficient knees.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".