Cross-Sectional Imaging of Internal Derangement of the Wrist with Arthroscopic Correlation
Bibliographic record
Abstract
Wrist arthroscopy has become an indispensable tool for the surgeon treating internal derangement of the wrist. The role of arthroscopy in both the diagnosis and treatment of intrinsic ligaments and triangular fibrocartilage complex (TFCC) pathology is well established. Arthroscopy remains a surgical procedure with potential complications, and it does not obviate the need for a careful history, physical examination, and conventional radiography. When the diagnosis remains unclear after these initial investigations, cross-sectional imaging studies play a valuable role in the assessment of internal derangement of the wrist. These studies include magnetic resonance imaging (MRI), magnetic resonance arthrography (MRA), and computed tomography arthrography (CTA), the choice of which depends on the specific clinical query. The radiologist must have exact knowledge of the performance of each diagnostic test to select the appropriate one and interpret it in a clinically relevant manner. With continued refinements in the technological aspects of cross-sectional imaging, its potential to replace diagnostic arthroscopy will surely be realized in the near future. This article focuses on the role of cross-sectional imaging and arthroscopy in the evaluation and management of wrist internal derangement, namely of intrinsic ligaments and TFCC pathology.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".