Intraobserver Reliability on Classifying Bursitis on Shoulder Ultrasound
Bibliographic record
Abstract
Purpose: Bursitis is a common musculoskeletal cause of shoulder pain and treatment varies, thus correctly diagnosing and grading bursitis is paramount in deciding management. Our aim was to assess reliability in grading shoulder bursitis on ultrasonography among fellowship trained musculoskeletal radiologists at our institution. Methods: Retrospective study of patients diagnosed with bursitis on ultrasonography. Single-sonographic images of the subacromial-subdeltoid bursa were collected for each patient and randomized to form a test-bank of varying degrees of bursitis. Three months after the test was administered, the cases were randomized and readministered. The radiologists graded each case as: within normal limits, mild, moderate or severe. Intraobserver variability was measured using Cohen’s kappa coefficient. Linear regression model was performed to assess correlation between years of experience and kappa. Results: 10 radiologists reviewed 70 cases of bursitis. Kappa values ranged from .53 to .91, indicating ‘moderate’ to ‘almost perfect’ variability amongst radiologists. A moderate positive correlation of improving variability ( r = .69) with increasing years of experience exists. Conclusion: Fellowship trained musculoskeletal radiologists were able to grade shoulder bursitis with moderate to almost perfect variability, with a positive correlation of improved variability with increasing experience. This may help clinicians choose the correct treatment more confidently in their patients with shoulder pain.
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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 | 0.044 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".