Prevalence of knee abnormalities in patients with osteoarthritis and anterior cruciate ligament injury identified with peripheral magnetic resonance imaging: a pilot study.
Bibliographic record
Abstract
OBJECTIVES: 1) To assess, with a peripheral magnetic resonance imaging system (pMRI), the prevalence of bony and soft tissue abnormalities in the knee joints of normal subjects, osteoarthritis (OA) patients, and individuals who have suffered an anterior cruciate ligament (ACL) rupture; and 2) to compare the prevalence among groups. METHODS: Magnetic resonance (MR) images of 28 healthy, 32 OA, and 26 ACL damaged knees were acquired with a 1.0-T pMRI system. Two radiologists graded the presence and severity of 9 MR image features: cartilage degeneration, osteophytes, subchondral cyst, bone marrow edema, meniscal abnormality, ligament integrity, loose bodies, popliteal cysts, and joint effusion. RESULTS: Ten of 28 healthy (35.7%), 24 of 26 ACL (92.3%), and all OA knees (100%) showed prevalent cartilage defects; 5 healthy (17.9%), 20 ACL (76.9%), and all OA knees (100%) had osteophytes; and 9 normal (32.1%), 21 ACL (80.8%), and 29 OA knees (90.6%) had meniscal abnormalities. One-half of the knees in the OA group (16 of 32, 50%) had subchondral cysts, and almost one-half had bone marrow edema (15 of 32, 46.9%). These features were not common in the ACL group (7.7%, and 11.5%, respectively) and were not observed in healthy knees. The OA group had the most severe cartilage defects, osteophytes, bone marrow edema, subchondral cysts, and meniscal abnormalities; the ACL group showed more severe cartilage defects, osteophytes, and meniscal abnormalities than did normal subjects. CONCLUSION: The results suggest that knees that have sustained ACL damage have OA-like reatures; most subjects (19 of 26, 73.1%) could be identified as in the early stage of OA. The prominent abnormalities present in ACL-damaged knees are cartilage defects, osteophytes, and meniscal abnormalities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".