Potential surrogate outcomes in individuals at high risk for incident knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To study potential surrogate outcomes for osteoarthritis (OA) incidence by evaluating the association of short-term changes in clinical and imaging biomarkers with long-term clinical knee OA incidence. DESIGN: Middle-aged women with overweight/obesity, but free of knee symptoms were recruited through their general practitioners. At baseline, after 2.5 years, and after 6.5 years, questionnaires, physical examination, radiographs, and Magnetic resonance imaging (MRI) scans were obtained. The percentage of knees with a minimal clinically important difference for knee pain severity, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain/stiffness/function, and joint space narrowing, and of those with progression/regression of medial knee alignment, chronic knee pain, radiographic osteophytes, and cartilage defects, bone marrow lesions, osteophytes, and effusion/synovitis on MRI were determined. For each of these potential surrogate outcomes with ≥10% improvement or progression in the population over 2.5 years, the association with incident clinical knee OA, defined using the combined ACR-criteria, after 6.5 years was determined. RESULTS: Most pre-defined potential surrogate outcomes showed ≥10% change in the population over 2.5 years, but only worsening of TF cartilage defects, worsening of TF osteophytes on MRI, and an increase in pain severity were significantly associated with greater clinical knee OA incidence after 6.5 years. These potential surrogate outcomes had high specificity and negative predictive value (89-91%) and low sensitivity and positive predictive value (20-28%) CONCLUSIONS: Worsening of TF cartilage defects and TF osteophytes on MRI, and increased pain severity could be seen as surrogate outcomes for long-term OA incidence. However, higher positive predictive values seem warranted for the applicability of these factors in future preventive trials.
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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 | 0.004 | 0.018 |
| 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 | 0.000 | 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".