Tibiofemoral contact and alignment in patients with anterior cruciate ligament rupture treated nonoperatively versus reconstruction
Bibliographic record
Abstract
AIMS: Anterior cruciate ligament (ACL) rupture commonly leads to post-traumatic osteoarthritis, regardless of surgical reconstruction. This study uses standing MRI to investigate changes in contact area, contact centroid location, and tibiofemoral alignment between ACL-injured knees and healthy controls, to examine the effect of ACL reconstruction on these parameters. METHODS: An upright, open MRI was used to directly measure tibiofemoral contact area, centroid location, and alignment in 18 individuals with unilateral ACL rupture within the last five years. Eight participants had been treated nonoperatively and ten had ACL reconstruction performed within one year of injury. All participants were high-functioning and had returned to sport or recreational activities. Healthy contralateral knees served as controls. Participants were imaged in a standing posture with knees fully extended. RESULTS: Participants' mean age was 28.4 years (SD 7.3), the mean time since injury was 2.7 years (SD 1.6), and the mean International Knee Documentation Subjective Knee Form score was 84.4 (SD 13.5). ACL injury was associated with a 10% increase (p = 0.001) in contact area, controlling for compartment, sex, posture, age, body mass, and time since injury. ACL injury was associated with a 5.2% more posteriorly translated medial centroid (p = 0.001), equivalent to a 2.6 mm posterior translation on a representative tibia with mean posteroanterior width of 49.4 mm. Relative to the femur, the tibiae of ACL ruptured knees were 2.3 mm more anteriorly translated (p = 0.003) and 2.6° less externally rotated (p = 0.010) than healthy controls. ACL reconstruction was not associated with an improvement in any measure. CONCLUSION: 2021;103-B(9):1505-1513.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".