The Relationship between Anterior Cruciate Ligament Injury and Osteoarthritis of the Knee
Bibliographic record
Abstract
Anterior cruciate ligament (ACL) tears are a common injury, particularly in the athletic and youth populations. The known association between ACL injury and subsequent osteoarthritis (OA) of the knee merits a more in-depth understanding of the relationship between the ACL-injured knee and osteoarthritis. ACL injury, especially with concomitant meniscal or other ligamentous pathology, predisposes the knee to an increased risk of osteoarthritis. ACL insufficiency results in deterioration of the normal physiologic knee bending culminating in increased anterior tibial translation and increased internal tibial rotation. This leads to increased mean contact stresses in the posterior medial and lateral compartments under anterior and rotational loading. However, surgical reconstruction of the ACL has not been shown to reduce the risk of future OA development back to baseline and has variability based on operative factors of graft choice, timing of surgery, presence of meniscal and chondral abnormalities, and surgical technique. Known strategies to prevent OA development are applicable to patients with ACL deficiency or after ACL reconstruction and include weight management, avoidance of excessive musculoskeletal loading, and strength training. Reconstruction of the ACL does not necessarily prevent osteoarthritis in many of these patients and may depend on several external variables.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".