Shear wave elastography reveals elevated infrapatellar fat pad stiffness in patients with early osteoarthritis symptoms after ACL reconstruction
Bibliographic record
Abstract
The infrapatellar fat pad (IPFP) plays an important role in knee biomechanics and inflammation, particularly following anterior cruciate ligament reconstruction (ACLR). This study investigated whether IPFP stiffness, measured with shear wave elastography, is associated with early symptoms of osteoarthritis (OA) in individuals within one year after ACLR. In this cross-sectional study, 24 participants underwent bilateral IPFP stiffness assessments using shear wave elastography. Participants were positioned supine with 20° knee flexion. The stiffness limb symmetry index (LSI) was calculated to normalize stiffness between the ACLR and contralateral limbs. Early OA symptoms were defined as scores ≤85 % on at least two of four subscales of the Knee Injury and Osteoarthritis Outcome Score (KOOS). Independent t -tests were used to evaluate group differences in IPFP stiffness LSI, and receiver operating characteristic curve analysis determined the optimal LSI threshold for discriminating between groups. Eleven participants (46 %) showed early OA symptoms. Participants with early OA symptoms exhibited a significantly higher IPFP stiffness LSI compared to those without symptoms (49.2 ± 48.7 % vs. -17.3 ± 34.4 %, p < 0.001). An optimal stiffness LSI threshold of 7.1 % was identified, achieving 90.9 % sensitivity, 92.3 % specificity, and an area under the curve of 0.94. Shear wave elastography shows potential as a non-invasive tool for detecting early IPFP stiffness changes associated with OA symptoms post-ACLR. These findings suggest that IPFP stiffness may be an early marker for OA risk, warranting further longitudinal studies to evaluate its progression and to further examine the clinical utility of shear wave elastography.
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 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 | 0.002 |
| 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) | 0.001 | 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".