The Impact of a Handheld Ultrasound Device in a Rheumatic Heart Disease Screening Program in Ethiopia
Bibliographic record
Abstract
Background: Rheumatic heart disease affects 33 million people in low and middle income countries and is the leading cause of cardiovascular death among children and young adults. Penicillin prophylaxis prevents progression in asymptomatic disease. Efforts to expand echocardiographic screening are focusing on simplified protocols, non-physician ultrasonographers, and portable ultrasound devices, including handheld ultrasound. Recent advances support the use of single-view screening protocols. With the increasing availability and low cost of handheld devices, studies are needed to evaluate their performance in these settings. Methods: We conducted a retrospective study comparing the rate of screen positive ultrasounds before and after the use of a handheld ultrasound in an RHD screening program in Ethiopia. We also performed a cross-sectional device comparison in 19 at-risk school-children participating in the rheumatic heart disease screening program. Results: Between March of 2019 and Jan of 2022, 6631 children were screened for rheumatic heart disease of whom 4029 were screened after the introduction of a handheld device. Before the use of the handheld ultrasound device 291 (11.2%) children had a screen positive ultrasounds compared with 167 (4.1%) afterwards (p < 0.001). We also compared non-expert to expert interpretation by device and found a significant difference in interpretation for the Lumify (p=0.025). There was a trend towards shorter jet length by color Doppler in the handheld ultrasound device for both expert and non-expert review. Conclusions: Our study highlights that the screen-positive rate in a RHD screening program is influenced by the device being used in the screening process.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".