Early Knee Osteoarthritis Is Evident One Year Following Anterior Cruciate Ligament Reconstruction: A Magnetic Resonance Imaging Evaluation
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and factors associated with knee osteoarthritis (OA) defined by magnetic resonance imaging (MRI) and specific OA features on MRI 1 year after anterior cruciate ligament reconstruction (ACLR). METHODS: Isotropic 3.0T MRI scans were obtained for 111 participants (71 men; mean ± SD age 30 ± 8 years) 1 year after ACLR as well as for 20 age-, sex-, and activity level-matched uninjured controls. The MRI OA Knee Score was used to score specific OA features. MRI-defined tibiofemoral and patellofemoral OA was evaluated based on published criteria. Logistic regression identified factors associated with MRI-defined OA and specific OA features after ACLR. RESULTS: Following ACLR, medial and lateral tibiofemoral OA on MRI was observed in 7 participants (6%) and 12 participants (11%), respectively, while 19 participants (17%) had patellofemoral OA on MRI. The femoral trochlea was the region most affected by bone marrow lesions (19% of participants), cartilage lesions (31% of participants), and osteophytes (37% of participants). Meniscectomy at the time of ACLR (odds ratio 6.8 [95% confidence interval 2.0-23.3]) and body mass index (BMI) >25 kg/m(2) (odds ratio 3.0 [95% confidence interval 1.3-6.9]) predicted MRI-defined tibiofemoral OA and osteophytes, respectively. Men had higher odds of patellofemoral osteophytes (odds ratio 6.3 [95% confidence interval 2.4-16.2]). No uninjured controls had tibiofemoral or patellofemoral OA on MRI, and specific OA features were uncommon. CONCLUSION: OA 1 year following ACLR was more common than previously recognized, while being absent in uninjured control knees. The patellofemoral compartment seems to be at particular risk for early OA after ACLR, especially in men. The association with meniscectomy and BMI demonstrates the construct validity of MRI criteria.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".