Juvenile Osteochondritis Dissecans of the Knee: Does Magnetic Resonance Imaging Instability Correlate With the Need for Surgical Intervention?
Bibliographic record
Abstract
BACKGROUND: Several magnetic resonance imaging (MRI) classification systems have been developed to determine the degree of instability of juvenile osteochondritis dissecans (JOCD) lesions. Our current practice involves correlating the patients' symptoms with their MRI findings, but there are many cases in which this correlation is poor and results in incorrect classification of the stability of a JOCD lesion. PURPOSE: To determine whether certain MRI findings of JOCD instability are associated with the necessity for arthroscopic intervention to assess lesion stability. A secondary objective was to determine the interobserver agreement of the Kijowski et al (2008) MRI criteria for osteochondritis dissecans (OCD) instability. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: A retrospective review was performed of patients who had documented femoral condyle OCD lesions, had open growth plates, and had been evaluated with serial MRI examinations. Each OCD lesion on MRI was classified according to the Kijowski classification as either stable or likely unstable. RESULTS: The study included 16 patients (17 knees) with 49 MRI examinations. The initial MRI lesion was graded as stable in 59% (10/17), and 7 of these remained stable throughout the study period, with only 1 requiring operative intervention. Two became unstable on MRI, with 1 requiring surgery and 1 reverting back to stable with nonoperative management. The initial MRI lesion was graded as unstable in 41% (7/17) of the knees, with 2 of these becoming stable on MRI during the follow-up period with nonoperative management. The other 5 lesions remained unstable, with 3 eventually requiring surgery. The most important finding in this study was that MRI instability is a poor predictor of the necessity for surgery for JOCD lesions. The correlation between a nonradiologist and a trained musculoskeletal radiologist in the interpretation of the MRI criteria by Kijowski et al showed only substantial agreement for multiple cysts. CONCLUSION: MRI criteria for OCD instability in the pediatric knee do not always correlate with the necessity for surgery. They are, at times, a helpful adjunct to the management of these patients; however, care must be taken to interpret all available clinical and radiographic information in surgical decision making.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".