Radiographic osteoarthritis and pain are independent predictors of knee cartilage loss: a prospective study
Bibliographic record
Abstract
BACKGROUND: There is controversy about whether pain and radiographic osteoarthritis (ROA) predict subsequent cartilage loss. The aim of this study was to describe the relationship between ROA, pain and cartilage loss in the knee. METHODS: We studied randomly selected subjects at baseline and approximately 2.9 years later (n= 399). The presence of ROA was assessed at baseline with a standing anteroposterior semiflexed radiograph scored using the Osteoarthritis Research Society International atlas for osteophytes (OP) and joint space narrowing (JSN). Pain was assessed by the Western Ontario McMaster Osteoarthritis Index. Subjects' medial and lateral tibial cartilage volumes were determined by magnetic resonance imaging at both time points. RESULTS: In cross-sectional analysis, both medial and lateral tibial cartilage volumes were lower in those with ROA. Any medial ROA predicted medial tibial cartilage loss (3.2% (standard deviation (SD) 5.6) vs 1.9% (SD 5.3) per annum) while any lateral ROA predicted both medial (4.0% (SD 6.0) vs 2.2% (SD 5.3) per annum) and lateral (3.5% (SD 5.8) vs 1.6% (SD 4.2) per annum) tibial cartilage loss (all P < 0.05). In multivariate analysis, JSN and OP at both medial and lateral sites had independent dose-response associations with tibial cartilage loss at both sites. Pain was an independent predictor of lateral, but not medial, tibial cartilage loss after taking ROA into account. CONCLUSIONS: Subjects with ROA (either JSN or OP) and, to a lesser extent, pain lose cartilage faster than subjects without ROA and the more severe the ROA the greater the rate of loss. These findings have implications for the design of clinical 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".