The development of a provincial multidisciplinary framework of consensus-based standards for Point of Care Ultrasound at the University of Saskatchewan
Bibliographic record
Abstract
OBJECTIVES: The development and adoption of Point-of-Care Ultrasound (POCUS) across disciplines have created challenges and opportunities in implementing training and utilization standards. Within the context of a large, geographically disparate province, we sought to develop a multidisciplinary POCUS framework outlining consensus-based standards. METHODS: A core working group of local POCUS leaders from Anesthesia, Emergency Medicine, Family Medicine, Intensive Care, Internal Medicine, Pediatrics, and Trauma, in collaboration with western Canadian colleagues, developed a list of key domains for the framework along with a range of potential standards for each area. The members of the working group and the registrants for a multidisciplinary Roundtable discussion at the University of Saskatchewan's annual POCUS conference (SASKSONO19, Saskatoon, Saskatchewan, March 2nd, 2019) were invited to complete a survey on POCUS standards for each domain. The survey results were presented to and discussed by participants at the Roundtable discussion at SASKSONO19 who reached consensus on modified standards for each domain. The modified standards were considered for endorsement by all conference attendees using an audience-response system. RESULTS: The working group proposed standards in eight domains: scope of use, credentialing and privileges, documentation, quality assurance, leadership and governance, teaching, research, and equipment maintenance. Consensus on modified standards was achieved in the 18 participant Roundtable. Each standard was then endorsed by > 90% of conference respondents. CONCLUSION: The resulting framework will inform the utilization of POCUS within Saskatchewan. Both this process and its outcomes could inform the development of multidisciplinary POCUS standards within other jurisdictions.
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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 | 0.219 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| 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".