Tool for osteoarthritis risk prediction (TOARP) over 8 years using baseline clinical data, X‐ray, and MRI: Data from the osteoarthritis initiative
Bibliographic record
Abstract
Background Osteoarthritis (OA), a multifactorial disease causing joint degeneration, often leads to severe disability. The rising rates of disability highlight the need for implementing preventative measures at early stages of the disease, which would especially benefit subjects at high risk for OA development. Purpose To develop a risk prediction tool for moderate‐severe OA (TOARP) over 8 years based on subject characteristics, knee radiographs, and MRI data at baseline using data from the Osteoarthritis Initiative (OAI). Study Type Retrospective. Subjects 641 subjects with no/mild radiographic OA (Kellgren–Lawrence [KL] 0–2) and no clinically significant symptoms (Western Ontario and McMaster Universities Arthritis Index [WOMAC] 0–1) were selected from the OAI. Field Strength/Sequence MR images were obtained using 3.0T. Assessment Compartment‐specific cartilage and meniscus morphology and cartilage T 2 were assessed. Baseline subject demographics, risk factors, KL score, cartilage WORMS score, presence of meniscus tear, and cartilage T 2 were used to predict the development of moderate/severe OA (KL = 3–4 or WOMAC pain ≥5 or total knee replacement [TKR]) over 8 years. Statistical Tests Best subsets variable selection followed by cross‐validation were used to assess which combinations of variables best predict moderate/severe OA. Results Model 1 included KL score, previous knee injury in the last 12 months, age, gender, and BMI. Model 2 included all variables in Model 1 plus presence of cartilage defects in the lateral femur and patella, and presence of a meniscal tear. Model 3 included all variables in Models 1 and 2, plus cartilage T 2 in the medial tibia and medial femur. Compared to Model 1 (cross‐validated AUC = 0.67), Model 3 performed significantly better (AUC = 0.72, P = 0.04), while Model 2 showed a statistical trend (AUC = 0.71, P = 0.08). Data Conclusion We established a risk calculator for the development of moderate/severe knee OA over 8 years that includes radiographic and MRI data. The inclusion of MRI‐based morphological abnormalities and cartilage T 2 significantly improved model performance. Level of Evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1517–1526.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".