Direct MR arthrography of cadaveric wrists: Comparison between MR imaging at 3.0T and 7.0T and gross pathologic inspection
Bibliographic record
Abstract
PURPOSE: To prospectively evaluate the diagnostic accuracy of magnetic resonance (MR) arthrography for the detection of articular cartilage abnormalities at 3.0T and 7.0T in cadaveric wrists. MATERIALS AND METHODS: MR imaging (MRI) was performed in nine cadaveric wrists (four right wrists, five left; mean age, 81.0 ± 9.8 years) after the intraarticular administration of gadoterate-meglumine. A 3.0T and 7.0T MR system, mechanically identical custom-built 8-channel wrist coil arrays and a similar standard MRI protocol, were used. MR images were evaluated for visibility of articular cartilage surfaces, presence of cartilage lesions, and confidence of diagnosis by two independent radiologists. Open pathologic inspection served as reference standard. Sensitivity, specificity, negative predictive values (NPV) and positive predictive values (PPV), and accuracy (ACC) were calculated. Wilcoxon signed rank test was used to assess differences in the diagnostic performance. RESULTS: Visibility of articular cartilage surfaces was significantly better at 3.0T than at 7.0T (P < 0.001). Mean sensitivity, specificity, NPV, PPV, ACC for both readers were 63%, 90%, 85%, 76%, 82% at 3.0T, respectively, and 52%, 91%, 82%, 75%, 79% at 7.0T. The difference between 3.0T and 7.0T was not significant for reader 1 (P = 0.51), but was significant for reader 2 (P = 0.01). The level of confidence was significantly higher at 3.0T than at 7.0T for both readers (P = 0.004; P = 0.03). CONCLUSION: MR arthrography of the wrist at 7.0T is still limited by the lack of commercially available radiofrequency coils and limited experience in sequence optimization, resulting in a significantly lower visibility of anatomy, lower diagnostic accuracy, and level of confidence in judging cartilage lesions compared to 3.0T.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".