LONGITUDINAL PROGRESSION OF TRAUMATIC BONE MARROW LESIONS FOLLOWING ANTERIOR CRUCIATE LIGAMENT INJURY: ASSOCIATIONS WITH KNEE PAIN AND CONCOMITANT INJURIES
Bibliographic record
Abstract
INTRODUCTION Traumatic BM lesions occur in about 80% of ACL injuries, typically caused by tibia-femur collisions, indicating significant joint damage and an increased risk of post-traumatic OA (PTOA). MRI is effective for detecting BM lesions, but quantitative assessment of their volume and distribution over time can help identify PTOA risk factors. While BM lesions typically resolve over time, their relationship with knee pain and functional outcomes remains unclear. OBJECTIVE This study aimed to investigate the longitudinal prevalence, characteristics, and progression of BM lesions following ACL injury, with a focus on their association with knee pain, ligamentous injuries, and meniscal tears. METHODS This prospective observational study analyzed data from 100 individuals (68 females, 32 males) with acute ACL tears in previously uninjured knees. MRI scans were obtained within 6 weeks of their injury using a 1.5-T MR scanner (GE OptimaMR430S, 1.5T, Waukesha, WI, USA). The imaging protocol included T2‐weighted fat‐suppressed fast spin echo images [TR/TE, 4300/56 ms; echo train length, 11; matrix, 320 × 256; field of view, 140 mm; slice thickness, 3.5 mm; gap, 0.3 mm;] for evaluating BM lesions. BM lesion volume quantified using a previously developed automated segmentation tool. Knee pain and symptoms were assessed using the Knee Injury and Osteoarthritis Outcome Score (KOOS). Statistical analyses included paired t-tests, Mann-Whitney U tests, Pearson’s Chi-squared test, and Spearman’s rank correlation. Multiple comparisons were corrected using the Benjamini-Hochberg procedure to control for false discovery rate. A subset of 77 participants completed follow-up KOOS surveys, and 19 participants who did not undergo ACL reconstruction had follow-up MRIs at one year. RESULTS BM lesions were present in 95% of participants (N=100), predominantly in the lateral tibial plateau and lateral femoral condyle. Males exhibited significantly higher BM lesion volumes than females (p = 0.03). Significant associations were identified between medial collateral ligament tears and both lateral collateral ligament (p = 0.01) and posterior cruciate ligament tears (p < 0.01). The BM lesion volume at baseline was negatively correlated with KOOS Symptoms at baseline (r = -0.270, p = 0.01). Longitudinal analyses revealed strong predictive relationships between baseline KOOS scores and future outcomes, with baseline KOOS Pain predicting follow-up Symptoms (r = 0.500) and Pain (r = 0.542). At the one-year follow-up, BM lesions in non-surgical participants (N=19) showed substantial resolution (mean change = -96.7%). Surgery had no significant impact on pain or functional outcomes compared to non-surgical participants. CONCLUSION BM lesion volume had only a weak association with knee pain after ACL injury, but longitudinal KOOS analyses revealed consistent associations between baseline symptoms and future outcomes.
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 | 0.003 |
| 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) | 0.002 | 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".