A Survey of Upper Extremity Musculoskeletal Ultrasound Use in a Surgical Practice
Bibliographic record
Abstract
Purpose: The purpose of this study is to gather the practices and perceptions among upper extremity surgeons regarding the use of musculoskeletal ultrasound for diagnostic and therapeutic intervention. Methods: A 36-question survey was developed from a literature review and author consensus. This survey was then piloted among a small group of hand surgeons prior to distribution. The survey included respondent characteristics, use of ultrasound in their current practice, referral patterns for ultrasound, and interest in incorporating ultrasound into practice and residency training. The refined survey was distributed to the Canadian Society of Plastic Surgeons, and Wrist and Elbow Society of Canada, as well as plastic surgery training programs. A reminder email was sent at 3 weeks and again at 8 weeks. Results: There were 152 responses after 505 survey invitations (30% response rate). Of these responses, 140 were complete (92%). Diagnostic ultrasound was used by 16 respondents (11%) for a myriad of pathologies. Only 5% used ultrasound for guided injections in a clinic or office setting. Money, time, lack of training, and lack of billing code were the major barriers to ultrasound implementation for 124 (89%) hand surgeons. Most respondents (84%) believe that ultrasound training should be incorporated into residency training. Ninety-one respondents (60%) are interested or very interested in incorporating ultrasound into their current practice. Conclusions: Ultrasound is a valuable resource that is seldom used as a point of care by Canadian hand surgeons due to several barriers. Survey results suggest that upper extremity surgeons are keen to have ultrasonography be part of residency education and most wish to adopt it into their future practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".