Injuries of the Scapholunate Interosseous Ligament
Bibliographic record
Abstract
To the Editor: I wish to clarify the statement made by White and Rollick1 that “MDCT [multidetector CT] was also noted to have superior detection of lunotriquetral tears, triangular fibrocartilage complex injuries, and cartilage abnormalities relative to MRA and MRI.” The authors reference a study by Moser et al,2 which did demonstrate superior sensitivity and specificity in detecting tears of the scapholunate and lunotriquetral ligaments, as well as the triangular fibrocartilage complex tears. However, it should be noted that, regarding the evaluation of cartilage, the study only localized the presence or absence of a defect. Furthermore, these were scored only as “abnormalities” and never defined or described. In effect, one cannot make the claim that MDCT is superior to MRI in the evaluation of cartilage. Furthermore, this particular study used T1-weighted magnetic resonance images and not the standard cartilage-sensitive pulse sequences, such as spoiled gradient-recalled echo or fast spin-echo techniques.3 These standard techniques have been in existence for over 15 years and are widely available. While it is true that there are advanced CT techniques, such as delayed quantitative CT arthrography (dQCTA), which is used for the evaluation of glycosaminoglycan (a representative of cartilage matrix), these techniques are not yet representative of standard clinical practice. Additionally, while advanced CT may have a future role in cartilage mapping,4 these techniques use ionizing radiation and should be avoided where possible, especially in younger patients. Standard MRI using a cartilage-sensitive sequence can show cartilage fissuring, delamination, and focal loss, as verified by arthroscopy, and orthopaedic surgeons should use these readily available MRI techniques for the evaluation of cartilage loss in their patients.5 Harry G. Greditzer IV, MD New York, NY The Author Replies: We kindly thank Dr. Greditzer for his detailed concern about the relative value of MDCT and MRI/MRA with respect to identification of cartilage abnormalities. The article is intended to provide a survey of imaging options and to update the reader about what is new in the advanced imaging sphere. We have reviewed the paper by Moser et al2 and agree that superiority in the identification of cartilage abnormalities cannot be definitively claimed. It is clear that MDCT and MRI/MRA are both excellent techniques for identification of cartilage abnormalities. MRI, in our experience, is technique and reader dependent. Each treating physician is going to have to review the literature and apply it to the resources available in his or her setting. Clearly MRI is not superior if the appropriate sequences are not being acquired, and clearly MDCT is not superior if it is not available in one’s specific setting. We appreciate Dr. Greditzer’s comments. Neil J. White, MD Natalie C. Rollick, MD Calgary, Alberta, Canada
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".