Comparative Analysis of Ultrasound and MRI in Diagnosing Rotator Cuff Injuries in a Tertiary Care Setting
Bibliographic record
Abstract
A BSTRACT Aim: The aim of this study is to assess the diagnostic accuracy of ultrasonography in comparison to magnetic resonance imaging for evaluating rotator cuff injuries, while also examining the reliability of ultrasonography as a primary imaging technique. Materials and Methods: A prospective observational study of 84 individuals focused on those with shoulder pain indicating rotator cuff disease. After an ultrasound, all patients had an MRI. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy were calculated for tendon and bursal structures. Results: The findings indicate that ultrasound exhibited the highest sensitivity for identifying supraspinatus tears at 83.85%. Additionally, it showed a notable specificity in assessing subscapularis (94.74%), biceps tendon (96.65%), and bursitis-related conditions. A statistically significant correlation was observed in the evaluation of the subscapularis using MRI, with a P value of 0.048. Conclusion: The high specificity and accessibility of this imaging tool underscore its importance as a primary option, while MRI continues to play a crucial role in addressing complex or posterior shoulder pathologies.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".