Critical Care Ultrasound Competency of Fellows and Faculty in Pulmonary and Critical Care Medicine: A Nationwide Survey
Bibliographic record
Abstract
Purpose: Competency assessment standards for Critical Care Ultrasonography (CCUS) for Graduate Medical Education (GME) trainees in pulmonary/critical care medicine (PCCM) fellowship programs are lacking. We sought to answer the following research questions: How are PCCM fellows and teaching faculty assessed for CCUS competency? Which CCUS teaching methods are perceived as most effective by program directors (PDs) and fellows. Methods: Cross-sectional, nationwide, electronic survey of PCCM PDs and fellows in accredited GME training programs. Results: PDs and fellows both reported the highest rates of fellow competence to use CCUS for invasive procedural guidance, but lower rates for assessment of deep vein thrombosis and abdominal organs. 54% and 90% of PDs reported never assessing fellows or teaching faculty for CCUS competency, respectively. PDs and fellows perceived hands-on workshops and directly supervised CCUS exams as more effective learning methods than unsupervised CCUS archival with subsequent review and self-directed learning. Conclusions: There is substantial variation in CCUS competency assessment among PCCM fellows and teaching faculty nationwide. The majority of training programs do not formally assess fellows or teaching faculty for CCUS competence. Guidelines are needed to formulate standardized competency assessment tools for PCCM fellowship programs.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".