Cardiac point of care ultrasound in resource limited settings to manage children with congenital and acquired heart disease
Bibliographic record
Abstract
BACKGROUND: In resource limited settings, children with cardiac disease present late, have poor outcomes and access to paediatric cardiology programmes is limited. Cardiac point of care ultrasound was introduced at several Médecins Sans Frontières sites to facilitate cardiopulmonary assessment. We describe the spectrum of disease, case management and outcomes of cases reviewed over the Telemedicine platform. METHODS: Previously ultrasound naïve, remotely placed clinical teams received ultrasound training on focussed image acquisition. The Médecins Sans Frontières Telemedicine platform was utilised for remote case and imaging review to diagnose congenital and acquired heart disease and guide management supported by a remotely situated paediatric cardiologist. RESULTS: Two-hundred thirty-three cases were reviewed between 2016 and 2018. Of 191 who underwent focussed cardiac ultrasound, diagnoses included atrial and ventricular septal defects 11%, atrioventricular septal defects 7%, Tetralogy of Fallot 9%, cardiomyopathy/myocarditis 8%, rheumatic heart disease 8%, isolated pericardiac effusion 6%, complex congenital heart disease 4% and multiple other diagnoses in 15%. In 17%, there was no identifiable abnormality while 15% had inadequate imaging to make a diagnosis. Cardiologist involvement led to management changes in 75% of cases with a diagnosis. Mortality in the entire group was disproportionately higher among neonates (38%, 11/29) and infants (20%, 16/81). There was good agreement on independent review of selected cases between two independent paediatric cardiologists. CONCLUSION: Cardiac point of care ultrasound performed by remote clinical teams facilitated diagnosis and influenced management in cases reviewed over a Telemedicine platform. This is a feasible method to support clinical care in resource limited settings.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".