No Evidence for the Superiority of 3 Tesla Magnetic Resonance Imaging Over 1.5 Tesla Magnetic Resonance Imaging for Diagnosing Wrist Ligamentous Lesions: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
PURPOSE: To determine the diagnostic accuracy of native magnetic resonance imaging (MRI) regarding different ligamentous lesions of the wrist and to analyze the influence of technical characteristics, such as field strength, application of fat saturation, 3-dimensional sequences, and wrist coils. METHODS: A systematic search was performed using MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases. Studies that were published before February 12, 2024, were included. All studies comparing the diagnostic accuracy of native wrist MRI with that of wrist arthroscopy for suspected ligamentous lesions were included. Results were analyzed by anatomic localization and technical aspects of the MRI. To assess the quality of included studies, we used the revised Quality Assessment of Diagnostic Accuracy Studies tool. RESULTS: The systematic search revealed 5,181 articles. Thirty-seven studies, reporting 3,893 ligamentous lesions, were eligible for inclusion. The studies displayed heterogeneity in terms of technical conditions, such as field strength, the use of wrist coils, the application of 3-dimensional sequences, and fat saturation. Research methods also varied. Overall sensitivity and specificity were 0.78 (0.66-0.86) and 0.81 (0.70-0.89) for 1.5 Tesla (1.5T) MRI, whereas sensitivity was 0.73 (0.68-0.78) and specificity was 0.90 (0.59-0.98) for 3 Tesla (3T) MRI. There was no significant difference between the 2 subgroups (P = .3807 and P = .4248). Sensitivity was 0.82 (0.75-0.87) for triangular fibrocartilage complex lesions, 0.63 (0.50-0.74) for scapholunate ligament tears, and 0.41 (0.25-0.60) for lunotriquetral ligament lesions. Specificity for triangular fibrocartilage complex lesions was 0.82 (0.73-0.89), for scapholunate ligament tears was 0.86 (0.73-0.93), and for lunotriquetral ligament lesions was 0.93 (0.81-0.98). CONCLUSIONS: The sensitivity and specificity of MRI are influenced by the anatomic location of the lesion and technical conditions. In terms of diagnostic accuracy, no significant difference was found between 1.5T and 3T MRI. LEVEL OF EVIDENCE: Level III, systematic review of Level II-III studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.083 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".