Bone Bruises Associated with ACL Rupture
Bibliographic record
Abstract
BACKGROUND: Different patterns of bone bruising are seen on magnetic resonance imaging in acute anterior cruciate ligament ruptures. These patterns may relate to the mechanism of injury. HYPOTHESIS: There is a correlation between the mechanism of anterior cruciate ligament injury and bone bruise patterns on magnetic resonance imaging. STUDY DESIGN: Cohort study; Level of evidence, 2. METHODS: Data regarding the mechanism of anterior cruciate ligament rupture were collected prospectively on patients who had anterior cruciate ligament reconstructions. Inclusion criteria included a clear history of mechanism, magnetic resonance imaging within 6 weeks of injury, and no previous knee injury. Patients were divided into noncontact and contact mechanism groups. Bone bruise frequency, location, depth, and intensity were analyzed using magnetic resonance imaging and correlated with the mechanism of injury. RESULTS: Two hundred fifty-six patients were identified; 100 met the inclusion criteria, 86 were in the noncontact mechanism group, and 14 were in the contact mechanism group. The proportion of bone bruises in the lateral compartment in both groups was higher (P < .001) than in the medial compartment. Bone bruising was more frequent, deeper, and more intense in the noncontact group, with frequency (P = .019) and intensity (P < .001) scores reaching significance at the lateral tibial plateau. Medial compartment bone bruising was seen more frequently than previously reported, particularly in the noncontact group. CONCLUSION: The noncontact mechanism appears to cause more severe bone bruising in both the medial and lateral compartments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".