Emergency medicine ultrasonography in rural communities.
Bibliographic record
Abstract
INTRODUCTION: The Canadian Association of Emergency Physicians (CAEP) published a position statement in 2006 encouraging immediate access to emergency medicine ultrasonography (EMUS) 24 hours a day, 7 days a week. However, barriers to advanced imaging care still exist in many rural hospitals. Our study investigated the current availability of EMUS in rural communities and physicians' ability to use this technology. METHODS: A literature review and interviews with rural physicians were conducted in the summer of 2010 to design a questionnaire focusing on EMUS. The survey was then sent electronically or via regular mail in November 2010 to all Ontario physicians self-identified as "rural." Descriptive statistics and the Fisher exact test were used to analyze the data. RESULTS: A total of 207 rural physicians responded to the survey (response rate 28.6%). Of the respondents, 70.9% were male, median age was 49 years and median year of graduation was 1988. The respondents had been in practice for a median of 20 years and had been in their present community for a median of 15 years. More than two-thirds of physicians (69.5%) practised in communities with populations of less than 10 000. Nearly three-quarters (72.6%) worked in a rural emergency department (ED). Almost all (96.9%) reported having access to ultrasonography in the hospital. However, only 60.6% had access to ultrasonography in the ED. Less than half (44.4%) knew how to perform ultrasonography, with 77.3% citing lack of training. Of those using EMUS, 32.5% were using it at least once per shift. The most common reason to use EMUS was to rule out abdominal aortic aneurysm (58.3%). Most respondents (71.5%) agreed or strongly agreed that EMUS is a skill that all rural ED physicians should have. CONCLUSION: Patients in many rural EDs do not have immediate access to EMUS, as advocated by CAEP. This gap in care needs to be addressed to ensure that all patients, no matter where they live, have access to this proven imaging modality.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".