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 is an invaluable tool in the diagnosis and management of many 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 point of care ultrasound 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 physician's experience with ultrasound was conducted, examining training, exposure and barriers to use. This paper-based survey was completed by the participants in the presence of study authors to improve compliance. Data was collected over a period of two months, from October to November 2014. Results: One hundred and five (105) physicians participated in the survey. Fifty-six physicians had prior training in ultrasonography from 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 undertaken other non-accredited ultrasound courses. All of them reported an improvement in their procedural skills by employing ultrasound. Perceived lack of time in the Emergency Department was the main barrier to scanning. Other hurdles included a deficiency of trained personnel for guidance, shortage of equipment and a lack of experience and interest. Hands on training were stated as the preferred method for enhancing ultrasonography skills. Conclusions: There has been underutilization of point-of-care ultrasound by emergency physicians. Availability of dedicated time, equipment, supervision and training will 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".