A case for mandatory ultrasound training for rural general practitioners: a commentary
Bibliographic record
Abstract
CONTEXT: Point-of-care ultrasound is a rapidly evolving technology that enables rapid diagnostic imaging to be performed at a patient's bedside, reducing time to diagnosis and minimising the need for patient transfers. This has significant applications for rural emergency and general practice, and could potentially prevent unnecessary transfers of patients from rural communities to more urban centres for the purpose of diagnostic imaging, reducing costs and preventing disruption to patients' lives. Meta-analyses on point-of-care ultrasound have reported extremely high sensitivity and specificity when detecting lung pathology, and the potential applications of the technology are substantial. A significant application of the technology is in the care of rural paediatric patients, where acute lower respiratory pathology is the most common cause of preventable deaths, hospitalisations, and emergency medical retrievals from remote communities for children under five. ISSUES: Although widely available, point-of-care ultrasound technology is not widely utilised in Australian emergency departments and general practices. Issues with comprehensive training, maintenance of skills, upskilling and quality assurance programs prevent physicians from feeling confident when utilising the technology. In Canada, point-of-care ultrasound training is part of the core competency training in the Royal College of Physicians of Canada emergency medicine fellowship program. Point-of-care ultrasound is widely used in rural practice, although lack of training, funding, maintenance of skills and quality assurance were still listed as barriers to use. LESSONS LEARNED: Point-of-care ultrasound is a highly sensitive and specific technology with wide potential applications. Issues with quality control and maintenance of skills are preventing widespread use. Coupling point-of-care ultrasound with telemedicine could help increase the usability and accessibility of the technology by reducing the issues associated with maintenance of skills and quality assurance.
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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 | 0.022 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.053 | 0.049 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".