Comprehensive comparative analysis of infrapatellar fat pad morphologies in a longitudinal knee osteoarthritis exploratory study: new insights into its role as an independent prognostic marker
Bibliographic record
Abstract
BACKGROUND: No established markers can effectively phenotype knee osteoarthritis (OA) patients into subgroups. Infrapatellar fat pad (IPFP) morphology data that can forecast disease symptoms, structural changes, and knee replacement (KR) are sparse and conflicting. This 96-month longitudinal exploratory study aimed to identify which IPFP morphological features were the most effective independent prognostic markers against these outcomes. METHODS: This longitudinal study analyzed 1075 target knees (one knee per participant) from the Osteoarthritis Initiative (OAI) progression cohort. Structural changes include cartilage, bone marrow lesions (BMLs), and joint effusion volumes assessed using automated and quantitative magnetic resonance imaging systems (MRI). The IPFP global and signal (hyper- and hypo-) intensity volumes and areas were assessed using MRI combined with a newly developed, fully automated neuron-driven technology. Symptoms were evaluated using WOMAC scores. Data on KR was obtained from the OAI database. Data were collected at baseline and 12, 24, 48 and 96 months and analyzed using a mixed model for repeated measures (MMRM) or ANCOVA. RESULTS: The baseline characteristics were mild to moderate knee OA. Over time, disease symptoms (WOMAC), cartilage volume, IPFP global and hypointense signal volumes, and maximal and hypointense signal areas decreased (all p≤0.001). Joint effusion and hyperintense signal volume and area increased (both p≤0.001). Associations were found between IPFP morphologies at inclusion and changes in cartilage volume (hypointense and hyperintense volumes, 48, 96 months, p≤0.04), BML volume (global volume 48 months, p=0.05; hyperintense area, 12 months, p≤0.04), and effusion volume (hypointense volume 48 months and hyperintense volume 96 months, p≤0.05). At inclusion, smaller IPFP sizes (below median) were associated with cumulative KR at 96 months (global and hypointense volumes, p≤0.04 and maximum area, p=0.05). CONCLUSION: This longitudinal exploratory study, leveraging a fully automated technology, highlights that i) IPFP volume (global and both signals) is superior to area metrics in predicting long-term structural changes in OA, and ii) smaller IPFP volume and area are linked with reduced need for KR. These findings provide new insights into the usefulness of IPFP morphology as a predictive biomarker of knee OA outcomes, offering a new approach to stratifying knee OA patients.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".