Remote diagnosis of congenital heart disease: the impact of telemedicine
Bibliographic record
Abstract
OBJECTIVES: To determine the accuracy of remote diagnosis of congenital heart disease (CHD) by real-time transmission of echocardiographic images via integrated services digital network (ISDN) lines, to assess the impact on patient management and examine cost implications. DESIGN: Prospective comparison of echocardiograms on infants with suspected significant CHD performed as follows: (1) hands-on evaluation and echocardiogram by a paediatrician at a district general hospital (DGH) followed by (2) transmission of the echocardiogram via ISDN 6 with guidance from a paediatric cardiologist and finally (3) hands-on evaluation and echocardiogram by a paediatric cardiologist. The economic analysis compares the cost of patient care associated with the telemedicine service with a hypothetical control group. SETTING: Neonatal units of three DGH and a UK regional paediatric cardiology unit. RESULTS: Echocardiograms were transmitted on 124 infants. In five cases scans were inadequate for diagnosis. Of the remaining 119 tele-echocardiograms, a follow-up echocardiogram was performed on 109/119 (92%). Major CHD was diagnosed in 39/109 infants (36%) and minor CHD in 45 (41%). The tele-echo diagnosis was accurate in 96% of cases (kappa=0.89). Unnecessary transfer to the regional unit was avoided in 93/124 patients (75%). Despite relatively high implementation costs, telemedicine care was substantially cheaper than standard care. Each DGH potentially saved money by utilising the telemedicine service (mean saving: pound728/patient). CONCLUSIONS: CHD is accurately diagnosed by realtime transmission of echocardiograms performed by paediatricians under live guidance and interpretation by a paediatric cardiologist. Remote diagnosis and exclusion of CHD affects patient management and may be cost saving.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".