Physician's perspective on point-of-care ultrasound: Experience at a tertiary care emergency department in Qatar
Bibliographic record
Abstract
Background: Point-of-care ultrasound (POCUS) is an invaluable tool in the diagnosis and management of conditions presenting to emergency departments across the world. It has also improved the success rate of invasive bedside procedures. Objectives: This study aimed to investigate the current utilization of POCUS in a large tertiary care emergency department in the Middle East and to identify barriers to its utilization. Methods: A cross-sectional survey of emergency physicians' experience with ultrasound was conducted, which included examining the training, exposure, and barriers to use. This paper-based survey was completed by the participants in the presence of the authors of this study to improve compliance. Data were collected over a period of two months, from October to November 2014. Results: A total of 105 physicians participated in the survey. Of these participants, 56 had undergone prior training in ultrasonography by successfully completing courses approved by the Royal College of Emergency Medicine in the United Kingdom, and the Royal College of Physicians and Surgeons of Canada. Twenty-two physicians had completed other non-accredited ultrasound courses. An improvement in ultrasound procedural skills was reported by all those who completed training. A perceived lack of time in the emergency department was the main barrier to scanning patients. Other shortcomings included a deficiency of trained personnel for guidance, shortage of equipment, and a lack of experience and interest among physicians. Hands-on training was considered the preferred method among physicians for enhancing ultrasonography skills. Conclusions: The study identified an underutilization of POCUS by emergency physicians. Availability of dedicated time, equipment, supervision, and training may help to increase its usage.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".