A Survey of Ultrasound Training in U.S. and Canadian Chronic Pain Fellowship Programs
Bibliographic record
Abstract
OBJECTIVE: To assess the current state of ultrasound training in U.S. and Canadian Chronic Pain Fellowship programs. DESIGN: U.S. as well as Canadian chronic pain fellowship programs were contacted via email and program directors were asked to complete a survey. The surveys were completed online using a questionnaire. SETTING: Questionnaire via email. PATIENTS: None. INTERVENTIONS: None. OUTCOME: To assess the current state of ultrasound training in U.S. and Canadian Chronic Pain Fellowship programs. MEASURES: Current teaching structure, types, and numbers of ultrasound-guided interventional pain procedures. RESULTS: Thirty-one responses (30.7%) from the 97 U.S. and four Canadian programs surveyed. Of the 31 programs that responded, 26 offered ultrasound training; five did not. These 31 programs averaged 4.1 fellows per year, majority 96.2% of the 26 programs taught ultrasound throughout the fellowship year. The type of ultrasound training varied, with the large majority 96.2% being patient based. Among 26 programs, 96.2% used ultrasound for peripheral nerve blocks, 76.9% used ultrasound for non-axial musculoskeletal injections, and 53.8% used ultrasound for axial nerve blocks. CONCLUSIONS: Chronic pain fellowships were teaching ultrasound-guided procedures to their fellows. The majority of the fellowships offered ultrasound training throughout the fellowship year. A majority of training was accomplished via hands-on experience with patients. Chronic pain fellows were receiving a majority of ultrasound training for peripheral nerve blocks, followed by nonaxial musculoskeletal blocks, with few axial nerve blocks being taught.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".