A call to action: improving access to cardiac MRI for diagnosis of immune checkpoint inhibitor related myocarditis in low and middle income countries
Bibliographic record
Abstract
Immune checkpoint inhibitor (ICI) therapy is a rapidly expanding pillar of cancer treatment, but it carries the risk of immune-related adverse events. Among the most fatal is ICI-related myocarditis (ICIRM). Cardiac magnetic resonance (CMR) imaging is the non-invasive current gold standard for diagnosis, with significant disparities regarding availability and utilisation. The vast majority of ICIRM cases are reported in high-income countries (HICs), reflecting not only patterns of ICI use, but also a profound diagnostic gap in low- and middle-income countries (LMICs). LMICs face barriers to CMR access, including a stark deficit of MRI scanners, with approximately 1 scanner per million people in LMICs versus 26 per million people in HICs, prohibitive costs, and a critical shortage of trained radiologists and cardiologists. The inequity means that as ICI therapy becomes increasingly accessible worldwide, patients in resource-limited settings will be at a high-risk of undiagnosed and untreated ICIRM. Our paper issues a call to address this critical healthcare disparity. To improve CMR access for ICIRM diagnosis in LMICs, a multi-pronged strategy is imperative - Governmental support and policy change to prioritise infrastructure investment and integrate CMR into national health strategies; targeted educational programmes, such as the SWiM and 'train the trainer' initiatives, to build local expertise in CMR acquisition and interpretation; adoption of technological innovations, including cost-effective rapid CMR protocols and artificial intelligence (AI) tools that can reduce scan times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".