A large infrapatellar fat pad protects against knee pain and lateral tibial cartilage volume loss
Bibliographic record
Abstract
INTRODUCTION: The infrapatellar fat pad (IPFP) is commonly resected during knee joint arthroplasty, but the ramifications of doing so are unclear. This longitudinal study determined whether the size of the IPFP (maximum cross-sectional area (CSA)) was associated with knee cartilage loss and the development of knee pain in adults without knee osteoarthritis (OA). METHODS: A total of 297 adults without American College of Rheumatology clinical criteria for a diagnosis of knee OA were recruited. Knee MRI was performed at baseline and an average of 2.3 years later. IPFP maximal CSA and tibial cartilage volume were measured from MRI. A large and small IPFP were defined by the median split, with a large IPFP defined by being in the highest 50%. Body composition was performed at baseline using bio-impedance. Knee pain was assessed at follow-up using the Western Ontario and McMaster University Osteoarthritis Index (WOMAC). RESULTS: A larger IPFP at baseline was associated with reduced knee pain at follow-up (OR 0.5, 95% CI: 0.3 to 0.9, p = 0.02) and lateral tibial cartilage volume loss (β: -0.9% (95% CI: -1.6, -0.1%) per annum, p = 0.03). The maximal CSA of the IPFP was predominantly located in the lateral (54.2%), rather than the medial tibiofemoral compartment (1.7%). Male gender (OR 12.0, 95% CI: 6.5 to 22.0, p < 0.001) and fat free mass (OR 1.15, 95% CI 1.04 to 1.28, p = 0.007) were both associated with a large IPFP. CONCLUSION: A larger IPFP predicts reduced lateral tibial cartilage volume loss and development of knee pain and mechanistically might function as a local shock-absorber. The lack of association between measures of adiposity and the size of the IPFP might suggest that the IPFP size is not simply a marker of systemic obesity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".