Structure and evaluation of a multidisciplinary point of care ultrasound guided central line program: An update
Bibliographic record
Abstract
Background: Point of Care Ultrasound (PoCUS) is well established within Emergency Medicine, however the availability of formal training for other clinical disciplines is limited. Recently, many other disciplines, including Internal Medicine, Surgery, Anesthesia, Obstetrics & Gynecology, and Rural Family Medicine, have recognized the clinical and educational benefits of PoCUS and are seeking to establish formal training programs. Memorial University has established a cost-efficient, multidisciplinary PoCUS training program that focuses on training residents discipline-specific ultrasound skills. This modular program consists of a combination of online education, practical training, competency development and subsequent knowledge transfer. The program tracks individual residents progress via a learning management system. Through learning, teaching and administration of this self-sustaining resident-driven PoCUS program, residents will reflect on and enhance development of their CanMEDS roles. Objectives To provide an update on the structure, implementation, and assessment of Memorial University’s Ultrasound Guided Central Line Program. Methods Assessment will consist of pre and post surveys of residents’ skill and comfort placing lines, reflection on the impact program had on CanMEDS roles development, and analysis of complication rates and instructor assessments during competency development. Also, trends in catheter related bloodstream infections using standardized hospital data before and during program implementation will be monitored. Conclusions: Memorial University’s Multidisciplinary Point of Care Ultrasound Program combines a new approach to train residents in ultrasound while using and developing the CanMEDS roles. This project will provide guidance to other Universities across Canada on the design and implementation of a cost-effective, multidisciplinary PoCUS training program incorporating the CanMEDS framework. The authors would like to thank the Medical Research Endowment Fund, Memorial University for its financial support of this initiative.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".