Feasibility of high resolution ultrasound for SLAP tears of the shoulder compared to MR arthrogram
Bibliographic record
Abstract
Objectives: The purpose of this prospective pilot study was to evaluate the feasibility and accuracy of high resolution ultrasound in the detection of superior labral anteroposterior (SLAP) tears of the shoulder compared to MR arthrogram. Methods and materials: 48 adult patients were included in the study. All patients had high resolution ultrasound of the superior labrum and biceps labral anchor prior to MR arthrogram. Ultrasound and MR arthrograms were evaluated separately for the presence or absence of SLAP tear using the same grading. The presence or absence of a tear and grading of the tears on MR arthrograms and ultrasound were compared and evaluated using κ statistics. Results: Both MRI and ultrasound demonstrated a SLAP tear in 27 of the 48 patients. MRI and ultrasound were in agreement on the absence of a tear in 19 patients. There was a disagreement between MRI and ultrasound in 2 of the 48 patients regarding the existence of a tear. The two modalities demonstrated substantial agreement on the presence or absence of a tear ( κ = 91.4 %, p < 0.001) as well as the grading of the tear ( κ = 84.4 %, p < 0.001). Conclusions: In this pilot study, the feasibility and accuracy of high resolution ultrasound for SLAP tears were evaluated and compared with MR arthrogram. MRI and ultrasound demonstrated substantial agreement on the presence or absence of SLAP tears and grading of the tears. Advances in knowledge: This pilot study explores and supports the use of ultrasound as a screening tool for SLAP tears, especially as it is readily available, fast and inexpensive.
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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 | 0.011 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".