Prevalence of small osteophytes on knee MRI in several large clinical and population-based studies of various age groups and OA risk factors
Bibliographic record
Abstract
Objective: Osteophytes, also small ones, are an important imaging feature of OA. However, due to their high prevalence on MR, the question has arisen whether these are truly pathophysiologic features of early OA, a result of physiologic aging, or rather a merely transient phenomenon. The aim of this study was to explore the prevalence of osteophytes on MR in various locations of the knee, with special emphasis on small osteophytes, across multiple large studies conducted in our institution comprising a wide range of subjects at different ages. Method: Retrospective explorative study of the prevalence of osteophytes, particularly grade 1 according to MOAKS, among four studies with a wide variety in age and OA risk factors. Results: A large number of grade 1 osteophytes were found in all four studies. The largest number of osteophytes were present in the youngest age group of <30 years (69.6%) compared to 36.8% in the age group of ≥30 < 50 years and 54,3% when aged ≥50 years, of which most were grade 1 osteophytes. Conclusion: Small osteophytes are highly prevalent among populations with varying age and OA risk factors, in particular among young subjects without other OA features. This might suggest that these "osteophytes" do not necessarily represent early OA, but rather indicate a transient physiologic phenomenon.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".