Association Between Infrapatellar Fat Pad Volume and Knee Structural Changes in Patients with Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The function of the infrapatellar fat pad (IPFP) in knee osteoarthritis (OA) remains uncertain. This study aimed to examine cross-sectional associations between IPFP volume and knee structures in patients with knee OA. METHODS: The study included 174 patients with clinical knee OA (mean age, 55.5 yrs). Fat-suppressed 3-D T1-weighted spoiled gradient recall magnetic resonance imaging (MRI) was used to measure the IPFP and cartilage volume. T2-weighted fast spin echo MRI was used to assess cartilage defects and bone marrow lesions (BML). Radiographic knee osteophytes and joint space narrowing (JSN) were assessed using the Osteoarthritis Research Society International atlas. RESULTS: After adjustment for potential confounders, greater IPFP volume was associated with greater tibial and patellar cartilage volume (all p < 0.05), and fewer cartilage defects at all sites (OR 0.88-0.91, all p < 0.05). IPFP volume was associated with presence of BML at lateral tibial and medial femoral sites (OR 0.88-0.91, all p < 0.05) and osteophytes at lateral tibiofemoral compartment (OR 0.88, p < 0.05). IPFP volume was not significantly associated with JSN. CONCLUSION: Greater IPFP volume was associated with greater knee cartilage volume and fewer structural abnormalities, suggesting a protective role of IPFP size in knee OA.
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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) | 0.001 | 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".