THE CLINICAL IMPORTANCE OF MENISCAL TEARS DEMONSTRATED BY MAGNETIC RESONANCE IMAGING IN OSTEOARTHRITIS OF THE KNEE☆
Bibliographic record
Abstract
BACKGROUND: Meniscal tears are frequently found during magnetic resonance imaging of osteoarthritic knees. However, the prevalence and clinical relevance of these tears have not been determined. This study was designed to investigate the relationship between meniscal tears and osteoarthritis and between such tears and pain in patients with osteoarthritis. METHODS: Magnetic resonance imaging and plain radiography of the knee were performed in a group of 154 patients with clinical symptoms of knee osteoarthritis and a group of forty-nine age-matched asymptomatic controls. Pain scores (according to a 100-mm visual analog scale) and functional scores (according to the Western Ontario and McMaster University Osteoarthritis Index [WOMAC]) were determined for ninety-one of the patients with symptomatic osteoarthritis. Meniscal tears were defined as tears extending to an articular surface as seen on magnetic resonance imaging. RESULTS: A medial or lateral meniscal tear was a very common finding in the asymptomatic subjects (prevalence, 76%) but was more common in the patients with symptomatic osteoarthritis (91%) (p < 0.005). In the group with symptomatic osteoarthritis, a higher Kellgren-Lawrence radiographic grade was correlated with a higher frequency of meniscal tears (r = 0.26, p < 0.001), and men had a higher prevalence of meniscal tears than did women (p < 0.01). However, there was no significant difference with regard to the pain or WOMAC score between the patients with and those without a medial or lateral meniscal tear in the osteoarthritic group (p = 0.8 to 0.9 for all comparisons). The power of the study was 80% to detect a difference in the WOMAC scores of 15 points and a difference in the scores on the visual analog scale of 16 mm. CONCLUSIONS: Meniscal tears are highly prevalent in both asymptomatic and clinically osteoarthritic knees of older individuals. However, osteoarthritic knees with a meniscal tear are not more painful than those without a tear, and the meniscal tears do not affect functional status. These data do not support the routine use of magnetic resonance imaging for the evaluation and management of meniscal tears in patients with osteoarthritis of the knee. LEVEL OF EVIDENCE: Diagnostic study, Level I-1 (testing of previously developed diagnostic criteria in series of consecutive patients [with universally applied reference "gold" standard]). See p. 2 for complete description of levels of evidence.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".