Point-of-care ultrasound training for respiratory therapists: A scoping review
Bibliographic record
Abstract
Introduction: Point-of-care ultrasound (POCUS), although commonly used in clinical practice, is not currently included in training programs for respiratory therapists (RTs). In fact, given its ubiquity and clinical utility, RTs in Ontario, Canada, are changing their mandate to incorporate POCUS into their daily patient assessment. Therefore, we conducted a scoping review of the literature, aiming to describe the current evidence of POCUS training and methods of curriculum delivery for RTs to inform an evidence-based program design. Method: We systematically searched MEDLINE, EMBASE, CINAHL, and Web of Science from inception to 8 July 2020. We included all studies reporting on RT training in POCUS. Documents included English language, full-text reports of all study designs. Title and abstract screening, full-text review, and data abstraction were done independently and in duplicate. Results: Seven studies met our inclusion criteria, including four full texts and three abstracts; all were prospective and single-center studies, except one multicenter study. Reports were from nine different countries. Studies described cardiac, lung, and procedural ultrasonography use. The majority used a combination of educational methods; didactic talks, hands-on sessions, and practical assessments being the most common methods. There was a median of 11 participants enrolled in a training session. The instructors were physicians from various specialties such as critical care, pulmonology, and radiology. Conclusions: This scoping review identified seven papers that explored different methods of a POCUS curriculum delivery for RTs. From the interventions outlined, teaching POCUS skills to RTs seems feasible. However, further work needs to be done to solidify a POCUS curriculum specific to RTs and examine the impact on patient-related outcomes.
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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 | 0.032 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".