Association of clinical findings with pre–radiographic and radiographic knee osteoarthritis in a population‐based study
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of pre-radiographic osteoarthritis (ROA) and ROA of the knee in a symptomatic population-based cohort, and to evaluate the clinical correlates of pre-ROA and ROA. METHODS: Subjects ages 40-79 years with knee pain were recruited as a random population sample and classified using magnetic resonance cartilage (MRC) scores (range 0-4) and Kellgren/Lawrence (K/L) scale grades (range 0-4) as no OA (MRC score<2, K/L grade<2), pre-ROA (MRC score ≥2, K/L grade<2), and ROA (MRC score≥2, K/L grade≥2). Logistic regression was used to evaluate the association of clinical variables with cartilage defects, comparing subjects with any cartilage defects (pre-ROA/ROA) with those without, and to determine associations with individual OA subgroups. RESULTS: Of 255 symptomatic subjects, no OA, pre-ROA, and ROA were seen in 13%, 49%, and 38%, respectively. The prevalence of pre-ROA/ROA compared with no OA was associated with age (odds ratio [OR] 2.89, 95% confidence interval [95% CI] 1.59-5.26), sports activity (OR 1.35, 95% CI 1.07-1.70), abnormal gait (OR 10.86, 95% CI 1.46-1,388.4), effusion (OR 16.58, 95% CI 2.22-2,120.5), and flexion contracture (OR 2.37, 95% CI 1.50-3.73). The prevalence of ROA versus no OA was significantly associated with age, body mass index, pain frequency, pain duration, severe knee injury, sports activity, gait, effusion, bony swelling, crepitus, flexion contracture, and flexion. The prevalence of pre-ROA versus no OA was increased with age, sports activity, effusion, and flexion contracture, and reduced with valgus malalignment. CONCLUSION: Cartilage defects were highly prevalent in this symptomatic population-based cohort, with 49% of subjects having pre-ROA and 38% having ROA. Prevalent cartilage defects were significantly associated with age, sports activity, abnormal gait, effusion, and flexion contracture.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".