Reliability of MRI Interpretation of Discoid Lateral Meniscus: A Multicenter Study
Bibliographic record
Abstract
Background: Discoid lateral meniscus (DLM) has a varied and complex morphology that can be challenging to assess and treat. Preoperative magnetic resonance imaging (MRI) is frequently used for diagnosis and surgical planning; however, it is not known whether surgeons are reliable and accurate in their interpretation of MRI findings when defining the pathomorphology of DLM. Hypothesis: Surgeons experienced in treating DLM are able to reliably interpret DLM pathology using MRI. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: Knee MRI scans from 44 patients (45 knees) were selected from a pool of surgically treated patients with DLM. Five reviewers (fellowship-trained pediatric sports medicine surgeons) performed independent review of each MRI scan using the PRiSM Discoid Meniscus Classification. Inter- and intraobserver reliability of the rating factors-primary (width, height, presence of peripheral instability or tear) and secondary (location of instability or tear, tear type)-was assessed using the Fleiss κ coefficient, designed for multiple readers with nominal variables (fair reliability, 0.21-0.40; moderate, 0.41-0.60; substantial, 0.61-0.80; excellent, 0.81-1.00). Reliability is reported as κ (95% CI). Results: Interobserver reliability in assessing most primary and secondary characteristics ranged from substantial (meniscal width) to moderate (peripheral instability, anterior instability, posterior instability, and posterior tear). Intraobserver reliability for most characteristics ranged from substantial (peripheral instability, presence of tear, anterior instability, posterior instability, and posterior tear) to moderate (meniscal width, anterior tear, and tear type). Notable exceptions were presence of tear, anterior tear, and tear type-all with fair interobserver reliability. Height had poor interobserver reliability and fair intraobserver reliability. Conclusion: Orthopaedic surgeons reliably interpret MRI scans using the PRiSM Discoid Meniscus Classification for the majority of DLM characteristics but vary in their assessment of height and presence and type of tear. MRI evaluation may be helpful to diagnose discoid by width and identify the presence of instability: 2 major factors in the decision to proceed with surgery. Arthroscopic evaluation should be used in conjunction with MRI findings for complete DLM diagnosis.
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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 | 0.026 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".