Medical Imaging: The Missing Element of National Health Plans
Bibliographic record
Abstract
Purpose: Radiology global outreach programs have increased in recent years but progressed more slowly than other specialties. Establishing radiology services is increasingly recognized as a priority in resource-limited settings. Myanmar has a tremendous disease burden that is treatable with interventional radiology (IR) techniques, and aims to grow and effectively integrate this service into its public healthcare sector. Through collaborations between Asia Pacific Society of Cardiovascular and Interventional Radiology (APSCVIR) and Myanmar Radiological Society (MRS), the field of IR has grown exponentially over recent years. This study aims to provide a Myanmar national IR report on the current trends and future challenges. Methods and materials: Descriptive variables across five domains (facility and equipment, workforce, supplies, infrastructure, and casemix) from the four public sector hospitals with IR capability were obtained between 2016-2019. The four hospitals were Yangon General Hospital (YGH), Yangon Specialty Hospital (YSH), Mandalay General Hospital (MGH), and Defense Services General Hospital (DSGH). Data were analyzed to demonstrate progress in IR and the differing casemix. Results: There are currently four IR-capable hospitals and nine interventional radiologists across Myanmar’s public healthcare sector. IR case volumes tripled from 514 cases in 2016 to more than 1,500 cases in 2019. The three most common procedures performed were trans-arterial chemoembolization (TACE, 63%), bronchial arterial embolization (BAE, 7.7%), and drainages (7.7%). Significant challenges to the growth and adoption of IR services span the domains of infrastructure, equipment and supplies, workforce, and IR awareness, among other clinical specialties. Conclusion: Myanmar’s healthcare priorities, coupled with international radiological outreach programs, have led to rapid growth of IR. The exponential growth in case volumes is promising for Myanmar and other developing countries. But to widen the scope of practice and integrate the service within local clinical workflows, a holistic effort that addresses multiple domains is needed in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".