Comparison of 2- and 3-Dimensional Shoulder Ultrasound to Magnetic Resonance Imaging in a Community Hospital for the Detection of Supraspinatus Rotator Cuff Tears with Improved Worktime Room Efficiency
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate whether 3-dimensional (3D) volumetric acquisition of shoulder ultrasound (US) data for supraspinatus rotator cuff tears is as sensitive when compared with conventional 2-dimensional (2D) US and routine magnetic resonance imaging (MRI), and whether there is improved workroom time efficiency when using the 3D technique compared with the 2D technique. METHODS: In this prospective study, 39 shoulders underwent US and MRI examination of their rotator cuff to confirm the accuracy of both the 2D and 3D techniques. The difference in sensitivities was compared by using confidence interval analysis. The mean times required to obtain the 2D and 3D US data and to review the scans were compared by using a 1-tailed Wilcoxon test. RESULTS: Sensitivity and specificity of 2D US in detecting supraspinatus full- and partial-thickness tears was 100% and 96%, and 80% and 100%, respectively, and similar values were obtained with 3D US at 100% and 100%, and 90% and 96.6%, respectively. Analysis of the confidence limits of the sensitivities showed no significant difference. The mean time (± SD) of the overall 2D examination of the shoulder, including interpretation was 10.02 ± 3.28 minutes, whereas, for the 3D examination, it was 7.08 ± 0.35 minutes. Comparison between the 2 cohorts when using a 1-tailed Wilcoxon test showed a statistically significant difference (P < .05). CONCLUSION: 3D US of the shoulder is as accurate as 2D US when compared with MRI for the diagnosis of full- and partial-thickness supraspinatus rotator cuff tears, and 3D US examination significantly reduced the time between the initial scan and the radiologist interpretation, ultimately improving workplace efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".