Knee effusion volume assessed by magnetic resonance imaging and progression of knee osteoarthritis: data from the Osteoarthritis Initiative
Bibliographic record
Abstract
Objective: To examine whether baseline knee joint effusion volume and the change in effusion volume over 1 year are associated with cartilage volume loss, progression of radiographic OA (ROA) over 4 years and risk of total knee replacement over 6 years. Methods: This study included 4115 Osteoarthritis Initiative participants with knee joint effusion volume quantified by MRI at baseline. The change in effusion volume over 1 year was assessed. Cartilage volume loss and progression of ROA over 4 years were assessed using MRI and X-ray and total knee replacement over 6 years was assessed. Multiple linear regression and binary logistic regression were used for data analyses. Results: Baseline knee effusion volume (per 5 ml) was positively associated with a loss of medial and lateral cartilage volume [regression coefficient 0.13%/year (95% CI 0.10, 0.17) and 0.13%/year (95% CI 0.10, 0.16), respectively, both P < 0.001], progression of ROA [odds ratio (OR) 1.28 (95% CI 1.20, 1.37), P < 0.001], and risk of knee replacement [OR 1.12 (95% CI 1.05, 1.20), P = 0.001]. A 5 ml increase in knee effusion volume over 1 year was positively associated with medial cartilage volume loss [regression coefficient 0.09%/year (95% CI 0.04, 0.15), P = 0.001], progression of ROA [OR 1.21 (95% CI 1.11, 1.33), P < 0.001] and risk of knee replacement [OR 1.24 (95% CI 1.12, 1.37), P < 0.001]. Conclusions: Knee joint effusion volume assessed from MRI provides a continuous and sensitive measure that was associated with cartilage volume loss, progression of ROA and risk of total knee replacement. It may provide a method to identify individuals with an inflammatory OA phenotype who are at higher risk of disease progression.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".