Defining Competencies for Ultrasound‐Guided Bedside Procedures
Bibliographic record
Abstract
OBJECTIVES: This study sought to define the competencies in ultrasound knowledge and skills that are essential for medical trainees to master to perform ultrasound-guided central venous catheterization, thoracentesis, and paracentesis. METHODS: Experts in the 3 procedures were identified by a snowball technique through 3 Canadian tertiary academic health centers. Experts completed 2 rounds of surveys, including an 88-item central venous catheterization survey, a 96-item thoracentesis survey, and an 89-item paracentesis survey. For each item, experts were asked to determine whether the knowledge/skill described was essential, important, or marginal. Consensus on an item was defined as agreement by at least 80% of the experts. For items on which consensus was not reached during the first round of surveys, a second survey was created in which the experts were asked to rate the item in a binary fashion (essential/important versus marginal/unimportant). RESULTS: Of the 27 experts invited to complete each survey, 25 (93%) completed the central venous catheterization survey; 22 (81%) completed the thoracentesis survey; and 23 (85%) completed the paracentesis survey. The experts represented 8 specialties from 8 cities within Canada. A total of 22, 32, and 28 items were determined to be essential competencies for central venous catheterization, thoracentesis, and paracentesis, respectively, whereas 47, 38, and 42 competencies were determined to be important, and 8, 13, and 10 were determined to be marginal. The ability to perform real-time direct ultrasound guidance was considered essential only for the performance of central venous catheterization insertion. CONCLUSIONS: Our study presents expert consensus-derived ultrasound competencies that should be considered during the design and implementation of procedural skills training for learners.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".