In Vivo Open-Bore MRI Reveals Region- and Sub-Arc-Specific Lengthening of the Unloaded Human Posterior Cruciate Ligament
Bibliographic record
Abstract
Open-bore MRI scanners allow joint soft tissue to be imaged over a large, uninterrupted range of flexion. Using an open-bore scanner, 3D para-sagittal images of the posterior cruciate ligament (PCL) were collected from seven healthy subjects in unloaded, recumbent knee extension and flexion. PCL length was measured from one 2D MRI slice partition per flexion angle, per subject. The anterior surface of the PCL lengthened significantly between extension and flexion (p<0.001). Conversely, the posterior surface did not. Changes were not due to the PCL moving relative to the 2D slice partition; measurements made from 3D reconstructions, which compensated for PCL movement, did not differ significantly from measurements made from 2D slice partitions. In a second experiment, videos of knee flexion were made by imaging two subjects at several flexion angles. Videos allowed soft tissue tracking; examples are included. In a third experiment, unloaded knees of seven healthy, recumbent subjects were imaged at extension and at 40°, 70°, 90°, 100°, 110° and 120° flexion. The distance between PCL attachments increased between extension and 100°, and then decreased (p<0.001). The anterior surface of the PCL lengthened over the flexion angles measured (p<0.01). The posterior surface of the PCL lengthened between extension and 40° and then shortened (p<0.001). Both attachment separation and anterior surface length increased dramatically between extension and 40°, but varied less afterwards. Results indicate that PCL dynamics differ between terminal extension and active function sub-arcs. Also, attachment separation cannot predict the lengthening of all parts of the PCL, nor can lengthening of one part of the PCL predict the lengthening of another part. A potential connection between lengthening and loading is discussed. We conclude that low-field MRI can assess ligament lengthening during flexion, and that the dynamics of the PCL for any given region and sub-arc should be measured directly.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".