MDCT Arthrography or MR Arthrography for Imaging the Wrist Joint?
Bibliographic record
Abstract
Imaging of the wrist joint has been radically modified over the last decade, particularly since multidetector computed tomography (MDCT) arthrography and magnetic resonance (MR) arthrography have become widely available. These two modalities allow a confident assessment of ligament tears and potential diagnosis of associated abnormalities of cartilage, bone, and soft tissues. The interosseous scapholunate and lunotriquetral ligaments and the triangular fibrocartilage complex (TFCC) are the most important structures to consider. Precise analysis of their different lesions, including recognition of degenerative tears, is essential for guiding the treatment. After a brief overview of the different injuries of interosseous ligaments and cartilage, this article thoroughly exposes the technical aspects of wrist MDCT arthrography and MR arthrography, reviews their results, and discusses their performances in light of recent literature. Finally, we propose an imaging strategy to decide between MDCT arthrography and MR arthrography depending on the clinical query. Other imaging modalities are not forgotten in this strategy. The evaluation of ligamentous and TFCC pathology must always begin with conventional radiographs. Cineradiography, ultrasound, and standard MRI are also useful in selected cases.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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".