The Role of Sonography in Differentiating Full Versus Partial Distal Biceps Tendon Tears: Correlation With Surgical Findings
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the accuracy of ultrasound for distinguishing complete rupture of the distal biceps tendon versus partial tear and versus a normal biceps tendon. Surgical findings were used as the reference standard in cases of tear. Clinical follow-up was used to assess the normal tendons. MATERIALS AND METHODS: The study population consisted of 45 consecutive elbow ultrasound cases with surgical confirmation and six cases of a clinically normal distal biceps tendon that underwent elbow ultrasound for suspicion of injury to a structure other than the biceps tendon. Cases underwent consensus review by two fellowship-trained musculoskeletal radiologists. Tendons were classified as normal biceps tendon, partial tear, or complete tear. The presence or absence of posterior acoustic shadowing at the distal biceps tendon was also assessed. The ultrasound findings were then compared with the surgical findings and clinical follow-up. RESULTS: Ultrasound showed 95% sensitivity, 71% specificity, and 91% accuracy for the diagnosis of complete versus partial distal biceps tendon tears. Posterior acoustic shadowing at the distal biceps had sensitivity of 97% and accuracy of 91% for indicating complete tear versus partial tear and sensitivity of 97%, specificity of 100%, and accuracy of 98% for indicating complete tear versus normal tendon. CONCLUSION: Ultrasound can play a role in the diagnosis of elbow injuries when a distal biceps brachii tendon tear is suspected.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".