Self‐directed learning of basic musculoskeletal ultrasound among rheumatologists in the United States
Bibliographic record
Abstract
OBJECTIVE: Because musculoskeletal ultrasound (MSUS) is highly user dependent, we aimed to establish whether non-mentored learning of MSUS is sufficient to achieve the same level of diagnostic accuracy and scanning reliability as has been achieved by rheumatologists recognized as international experts in MSUS. METHODS: A group of 8 rheumatologists with more experience in MSUS and 8 rheumatologists with less experience in MSUS participated in an MSUS exercise to assess patients with musculoskeletal abnormalities commonly seen in a rheumatology practice. Patients' established diagnoses were obtained from chart review (gout, osteoarthritis, rotator cuff syndrome, rheumatoid arthritis, and seronegative arthritis). Two examining groups were formed, each composed of 4 less experienced and 4 more experienced examiners. Each group scanned 1 predefined body region (hand, wrist, elbow, shoulder, knee, or ankle) in each of 8 patients, blinded to medical history and physical examination. Structural abnormalities were noted with dichotomous answers, and an open-ended answer was used for the final diagnosis. RESULTS: Less experienced and more experienced examiners achieved the same diagnostic accuracy (US-established diagnosis versus chart review diagnosis). The interrater reliability for tissue pathology was slightly higher for more experienced versus less experienced examiners (kappa = 0.43 versus kappa = 0.34; P = 0.001). CONCLUSION: Non-mentored training in MSUS can lead to the achievement of diagnostic accuracy in MSUS comparable to that achieved by highly experienced international experts. Reliability may increase slightly with additional experience. Further study is needed to determine the minimal training requirement to achieve proficiency in MSUS.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".