What Really Happens during the Lachman Test?
Bibliographic record
Abstract
BACKGROUND: The Lachman test is the most reliable clinical test for diagnosing rupture of the anterior cruciate ligament (ACL). It is generally believed that the lateral side contributes more than the medial side to anterior tibial translation, but this has never been convincingly proven. The MRI study presented in this article addresses this issue. HYPOTHESIS: The lateral side of the ACL-deficient knee contributes more than the medial side to anterior tibial translation. STUDY DESIGN: Prospective cohort study. METHODS: The Lachman test and the radiologic Lachman test were performed on 10 patients with isolated rupture of the ACL while the knee was scanned dynamically using open-access magnetic resonance imaging. The amount of movement in the midmedial and midlateral compartments of both the contralateral normal knee and the ACL-deficient knee was measured. RESULTS: In both normal and ACL-deficient knees, the lateral compartment contributes more than the medial to anterior translation of the tibia. Rupture of the ACL leads to increased laxity in both medial and lateral compartments with a statistically significant greater contribution from the lateral side. CONCLUSION: Rupture of the ACL leads to increased anterior tibial translation coupled with tibial internal rotation.
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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 | 0.008 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".