A Novel In Vivo Quantitative Assessment of the Knee Using High Resolution Peripheral Quantitative Computed Tomography
Bibliographic record
Abstract
INTRODUCTION: Anterior cruciate ligament (ACL) tears are a common knee injury and increase the risk of developing osteoarthritis (OA). While the sequence of events leading to OA is poorly understood, evidence suggests rapid changes in subchondral bone mineral density (BMD) in the injured knee after the ACL tear may play a role. How these changes are reflected in the bone microarchitecture is not understood, largely because clinical imaging modalities lack the required resolution to visualize the microarchitecture. High resolution peripheral quantitative computer tomography (HR-pQCT), a novel human in vivo micro computed tomography system, is able to measure bone microarchitecture. METHODS: Thirty-five participants were imaged with HR-pQCT at an isotropic resolution of 61 µm after undergoing varying types of ACL reconstructions five year prior. Approximately 6 cm of each knee was imaged. The medial and lateral weight bearing regions of both tibia and femur were analyzed. The subchondral bone in these regions was isolated and trabecular thickness, number, and BMD were measured at three depths (0-2.5mm, 2.5-5mm, 5-7.5mm) below the subchondral bone surface as well as cortical thickness and porosity. RESULTS: Scans took approximately 22 minutes per knee and provided rich 3D data showing bone morphology and microarchitecture. Bone microarchitectural parameters were successfully calculated for all regions and depths. CONCLUSIONS: We performed the first in vivo high resolution bone microarchitecture measurements of the human knee. Ongoing work includes establishing quantitative differences between lateral and medial bone compartments in the cohort, and characterizing differences between ACL deficient and healthy control knees.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".